hebrew-voice-bot-builder

name	hebrew-voice-bot-builder
description	Build Hebrew voice bots and IVR (Interactive Voice Response) systems with speech-to-text, text-to-speech, and telephony integration for Israeli businesses. Use when user asks to "build a Hebrew voice bot", "create an IVR in Hebrew", "Hebrew speech-to-text", "binui bot koli b'ivrit", "maarechet maane koli", "zihui dibur b'ivrit", or "Twilio Israel". Covers OpenAI Whisper Hebrew, Google Cloud STT/TTS he-IL, Azure Speech Services, Amazon Polly Hebrew, IVR menu design for Sunday-Thursday business hours, voicemail transcription, Hebrew accent handling, and +972 phone integration via Twilio and Vonage. Activate for: בוט קולי בעברית, מערכת מענה קולי, מענה אנושי, זיהוי דיבור בעברית, הקראת טקסט, המרת דיבור לטקסט, IVR בעברית, תמלול הודעות, מרכזייה טלפונית. Do NOT use for text-based chatbots (use hebrew-chatbot-builder), Hebrew NLP without voice (use hebrew-nlp-toolkit), or SMS messaging (use israeli-sms-gateway).
license	MIT
allowed-tools	Bash(python:*)
compatibility	Requires API keys for speech services (OpenAI, Google Cloud, Azure, or AWS). Requires Twilio or Vonage account for telephony. Works with Claude Code, Cursor, Windsurf.

Hebrew Voice Bot Builder

Build production-ready Hebrew voice bots and IVR systems for Israeli businesses. This skill covers the full voice pipeline: speech-to-text (STT), text-to-speech (TTS), IVR flow design, telephony integration, and Hebrew-specific challenges like accent handling and mixed Hebrew-English speech.

Instructions

Step 1: Choose Your Architecture

Before building, decide on the voice bot architecture based on the use case:

Architecture	Best For	Components
IVR (keypad)	Simple menu navigation, payment lines, appointment scheduling	TTS + DTMF + telephony
Voice bot (conversational)	Customer service, order status, FAQ handling	STT + LLM + TTS + telephony
Voicemail transcription	Missed call handling, message routing	STT + notification pipeline
Hybrid	Complex flows with both speech and keypad input	STT + TTS + DTMF + telephony

Key decisions:

• STT provider: OpenAI gpt-4o-transcribe or gpt-4o-mini-transcribe (best Hebrew WER and lower latency than whisper-1, available via the OpenAI Realtime API for streaming), whisper-large-v3-turbo for self-host, ivrit-ai's Hebrew-tuned variants (ivrit-ai/whisper-large-v3-turbo-ct2) for the best open Hebrew WER, Google Cloud STT (low latency), Azure Speech (enterprise features). The legacy whisper-1 API is still supported but gpt-4o-transcribe is the current default for Hebrew.
• TTS provider — split by use case:
  • Real-time / streaming (voice agents, IVR, live conversation): OpenAI Realtime API (gpt-4o-realtime) — native multilingual speech-to-speech including Hebrew, the 2026 default for sub-500ms turn-taking. Inworld TTS-1.5 / Realtime TTS-2 — explicitly lists Hebrew, sub-130ms P90 latency, designed for live conversation. Deepdub Phantom X 3.2 — Israeli company, real-time Hebrew with emotive eTTS, launched March 2026. ElevenLabs eleven_flash_v2_5 — WebSocket-capable but Hebrew quality is weak, use only when latency dominates over quality.
  • Offline / max quality (audiobooks, voicemail playback, batch generation): ElevenLabs eleven_v3 — best Hebrew quality ElevenLabs offers, supports Hebrew (heb) among 70+ languages but NO WebSocket / streaming API, REST only. Deepdub Phantom X 3.2 also serves this track with emotional control.
  • Fallbacks: Azure Neural TTS (he-IL-HilaNeural, he-IL-AvriNeural), Google Cloud TTS Wavenet (he-IL-Wavenet-A/B), Amazon Polly Hebrew (Avri only, no neural Hebrew voice as of Apr 2026, verify before relying on it). ElevenLabs Multilingual v2 lists Hebrew in its 29-language table but real-world Hebrew quality is mediocre; Turbo v2.5 was not designed with Hebrew in mind.
• Telephony: Twilio (largest Israeli number inventory), Vonage (competitive pricing for Israel).
• Hosting: Cloud functions for low-volume, dedicated servers for high-volume.
• Recording-consent disclosure: Israeli outbound automated calls must disclose recording at the start of the call (השיחה מוקלטת). Privacy Protection Law Amendment 13 (in force Aug 2025) tightens consent requirements for biometric voice data.

Step 2: Hebrew Speech-to-Text (STT)

OpenAI Whisper (Recommended for Accuracy)

Whisper provides the best Hebrew transcription accuracy, especially for mixed Hebrew-English speech common in Israeli tech environments.

import openai

client = openai.OpenAI()

def transcribe_hebrew(audio_file_path: str) -> str:
    """Transcribe Hebrew audio using OpenAI Whisper."""
    with open(audio_file_path, "rb") as audio_file:
        transcript = client.audio.transcriptions.create(
            model="whisper-1",
            file=audio_file,
            language="he",  # Force Hebrew language detection
            response_format="text",
        )
    return transcript


def transcribe_hebrew_with_timestamps(audio_file_path: str) -> dict:
    """Transcribe with word-level timestamps for subtitle generation."""
    with open(audio_file_path, "rb") as audio_file:
        transcript = client.audio.transcriptions.create(
            model="whisper-1",
            file=audio_file,
            language="he",
            response_format="verbose_json",
            timestamp_granularities=["word"],
        )
    return transcript

Whisper Hebrew tips:

• Set language="he" explicitly to avoid misdetecting Hebrew as Arabic
• For mixed Hebrew-English, let Whisper auto-detect (omit the language parameter) and post-process
• Whisper handles niqqud-free text well (standard for modern Hebrew)
• Audio quality matters: 16kHz+ sample rate, mono channel, WAV or FLAC preferred
• Maximum file size: 25MB. For longer recordings, split into segments

Google Cloud Speech-to-Text

Lower latency than Whisper, suitable for real-time voice bots.

from google.cloud import speech_v1

def transcribe_hebrew_google(audio_content: bytes) -> str:
    """Transcribe Hebrew audio using Google Cloud STT."""
    client = speech_v1.SpeechClient()

    audio = speech_v1.RecognitionAudio(content=audio_content)
    config = speech_v1.RecognitionConfig(
        encoding=speech_v1.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=16000,
        language_code="he-IL",
        # Enable automatic punctuation for Hebrew
        enable_automatic_punctuation=True,
        # Model optimized for phone calls
        model="phone_call",
        # Enable word-level confidence scores
        enable_word_confidence=True,
    )

    response = client.recognize(config=config, audio=audio)

    results = []
    for result in response.results:
        results.append(result.alternatives[0].transcript)

    return " ".join(results)


def stream_transcribe_hebrew(audio_generator):
    """Real-time streaming transcription for live phone calls."""
    client = speech_v1.SpeechClient()

    config = speech_v1.StreamingRecognitionConfig(
        config=speech_v1.RecognitionConfig(
            encoding=speech_v1.RecognitionConfig.AudioEncoding.MULAW,
            sample_rate_hertz=8000,  # Standard phone audio
            language_code="he-IL",
            model="phone_call",
            enable_automatic_punctuation=True,
        ),
        interim_results=True,  # Get partial results for faster response
    )

    streaming_config = speech_v1.StreamingRecognizeRequest(
        streaming_config=config
    )

    def request_generator():
        yield streaming_config
        for chunk in audio_generator:
            yield speech_v1.StreamingRecognizeRequest(audio_content=chunk)

    responses = client.streaming_recognize(requests=request_generator())

    for response in responses:
        for result in response.results:
            if result.is_final:
                yield result.alternatives[0].transcript

Azure Speech Services

Enterprise-grade with custom model training for domain-specific Hebrew vocabulary.

import azure.cognitiveservices.speech as speechsdk

def transcribe_hebrew_azure(audio_file_path: str) -> str:
    """Transcribe Hebrew audio using Azure Speech Services."""
    speech_config = speechsdk.SpeechConfig(
        subscription="YOUR_AZURE_KEY",
        region="westeurope",  # Closest region to Israel
    )
    speech_config.speech_recognition_language = "he-IL"

    audio_config = speechsdk.AudioConfig(filename=audio_file_path)
    recognizer = speechsdk.SpeechRecognizer(
        speech_config=speech_config,
        audio_config=audio_config,
    )

    result = recognizer.recognize_once()

    if result.reason == speechsdk.ResultReason.RecognizedSpeech:
        return result.text
    elif result.reason == speechsdk.ResultReason.NoMatch:
        return ""
    else:
        raise RuntimeError(f"Speech recognition failed: {result.reason}")

Consult references/hebrew-stt-models.md for a detailed comparison of STT providers with accuracy benchmarks.

Step 3: Hebrew Text-to-Speech (TTS)

Google Cloud TTS (Recommended for Natural Sound)

from google.cloud import texttospeech

def synthesize_hebrew(text: str, output_path: str, voice_gender: str = "female") -> None:
    """Convert Hebrew text to speech using Google Cloud TTS."""
    client = texttospeech.TextToSpeechClient()

    input_text = texttospeech.SynthesisInput(text=text)

    # Available Hebrew voices
    voice_name_map = {
        "female": "he-IL-Wavenet-A",  # Female, high quality
        "male": "he-IL-Wavenet-B",    # Male, high quality
        "female_standard": "he-IL-Standard-A",  # Female, lower cost
        "male_standard": "he-IL-Standard-B",    # Male, lower cost
    }

    voice = texttospeech.VoiceSelectionParams(
        language_code="he-IL",
        name=voice_name_map.get(voice_gender, "he-IL-Wavenet-A"),
    )

    audio_config = texttospeech.AudioConfig(
        audio_encoding=texttospeech.AudioEncoding.MP3,
        speaking_rate=1.0,   # 0.5 to 2.0, adjust for clarity
        pitch=0.0,           # -20.0 to 20.0 semitones
    )

    response = client.synthesize_speech(
        input=input_text, voice=voice, audio_config=audio_config
    )

    with open(output_path, "wb") as out:
        out.write(response.audio_content)

Amazon Polly Hebrew

Cost-effective for high-volume TTS needs.

import boto3

def synthesize_hebrew_polly(text: str, output_path: str) -> None:
    """Convert Hebrew text to speech using Amazon Polly."""
    polly = boto3.client("polly", region_name="eu-west-1")

    response = polly.synthesize_speech(
        Text=text,
        OutputFormat="mp3",
        VoiceId="Avri",  # Hebrew male standard voice (no neural Hebrew voice exists)
        Engine="standard",   # Standard engine (neural not available for Hebrew)
        LanguageCode="he-IL",
    )

    with open(output_path, "wb") as out:
        out.write(response["AudioStream"].read())

Azure Neural TTS

Highest quality Hebrew voices with SSML support for fine-grained control.

import azure.cognitiveservices.speech as speechsdk

def synthesize_hebrew_azure(text: str, output_path: str) -> None:
    """Convert Hebrew text to speech using Azure Neural TTS."""
    speech_config = speechsdk.SpeechConfig(
        subscription="YOUR_AZURE_KEY",
        region="westeurope",
    )
    # Hebrew neural voices
    speech_config.speech_synthesis_voice_name = "he-IL-HilaNeural"  # Female
    # Alternative: "he-IL-AvriNeural" for male voice

    audio_config = speechsdk.AudioConfig(filename=output_path)
    synthesizer = speechsdk.SpeechSynthesizer(
        speech_config=speech_config,
        audio_config=audio_config,
    )

    result = synthesizer.speak_text(text)

    if result.reason != speechsdk.ResultReason.SynthesizingAudioCompleted:
        raise RuntimeError(f"Speech synthesis failed: {result.reason}")


def synthesize_hebrew_ssml(ssml: str, output_path: str) -> None:
    """
    Synthesize Hebrew speech with SSML for fine control.

    Example SSML for IVR prompt:
    <speak version="1.0" xml:lang="he-IL">
        <voice name="he-IL-HilaNeural">
            <prosody rate="0.9">
                ברוכים הבאים לשירות הלקוחות.
            </prosody>
            <break time="500ms"/>
            לתמיכה טכנית, הקישו 1.
            <break time="300ms"/>
            למכירות, הקישו 2.
        </voice>
    </speak>
    """
    speech_config = speechsdk.SpeechConfig(
        subscription="YOUR_AZURE_KEY",
        region="westeurope",
    )
    audio_config = speechsdk.AudioConfig(filename=output_path)
    synthesizer = speechsdk.SpeechSynthesizer(
        speech_config=speech_config,
        audio_config=audio_config,
    )

    result = synthesizer.speak_ssml(ssml)
    if result.reason != speechsdk.ResultReason.SynthesizingAudioCompleted:
        raise RuntimeError(f"SSML synthesis failed: {result.reason}")

Step 4: IVR Menu Design for Israeli Businesses

Israeli IVR systems have specific conventions that differ from US/European patterns.

Business Hours Routing

Israeli business week is Sunday through Thursday. IVR systems must account for this:

from datetime import datetime
import pytz

ISRAEL_TZ = pytz.timezone("Asia/Jerusalem")

def get_business_status() -> dict:
    """Determine current business status for IVR routing."""
    now = datetime.now(ISRAEL_TZ)
    day = now.weekday()  # 0=Monday, 6=Sunday
    hour = now.hour

    # Israeli business days: Sunday (6) through Thursday (3)
    # Friday (4): half day until ~13:00
    # Saturday (5): closed (Shabbat)

    if day == 5:  # Saturday (Shabbat)
        return {
            "status": "closed",
            "reason": "shabbat",
            "message_he": "שלום, אנחנו סגורים בשבת. נחזור אליכם ביום ראשון.",
            "next_open": "Sunday 9:00",
        }
    elif day == 4:  # Friday
        if hour < 9:
            return {"status": "before_hours", "message_he": "שעות הפעילות ביום שישי: 9:00 עד 13:00."}
        elif hour < 13:
            return {"status": "open", "message_he": "שלום, איך אפשר לעזור?"}
        else:
            return {
                "status": "closed",
                "reason": "friday_afternoon",
                "message_he": "סגורים בשישי אחה\"צ. נחזור ביום ראשון.",
                "next_open": "Sunday 9:00",
            }
    elif day == 6 or day <= 3:  # Sunday through Thursday
        if 9 <= hour < 17:
            return {"status": "open", "message_he": "שלום, איך אפשר לעזור?"}
        else:
            return {
                "status": "after_hours",
                "message_he": "שעות הפעילות שלנו: א'-ה' 9:00-17:00, ו' 9:00-13:00.",
            }
    else:  # Should not happen but handle gracefully
        return {"status": "closed", "message_he": "כרגע אנחנו סגורים."}

Standard Israeli IVR Menu Structure

IVR_MENU = {
    "welcome": {
        "prompt_he": "שלום, הגעתם ל{company_name}.",
        "prompt_en": "Hello, you've reached {company_name}. For English, press 9.",
    },
    "main_menu": {
        "prompt_he": (
            "לשירות לקוחות, הקישו 1. "
            "למכירות, הקישו 2. "
            "לתמיכה טכנית, הקישו 3. "
            "למצב הזמנה, הקישו 4. "
            "לשמוע שוב, הקישו כוכבית."
        ),
        "options": {
            "1": "customer_service",
            "2": "sales",
            "3": "tech_support",
            "4": "order_status",
            "9": "english_menu",
            "*": "main_menu",  # Repeat
        },
        "timeout_seconds": 8,
        "no_input_prompt_he": "לא קיבלנו בחירה. בבקשה הקישו מספר מ-1 עד 4.",
        "invalid_prompt_he": "בחירה לא תקינה. נסו שוב.",
        "max_retries": 3,
    },
    "customer_service": {
        "prompt_he": (
            "לבירור חשבון, הקישו 1. "
            "לתלונה, הקישו 2. "
            "לנציג, הקישו 0. "
            "לחזרה לתפריט הראשי, הקישו כוכבית."
        ),
        "options": {
            "1": "account_inquiry",
            "2": "complaint",
            "0": "agent_queue",
            "*": "main_menu",
        },
    },
    "agent_queue": {
        "prompt_he": "ממתינים לנציג הפנוי הבא. זמן המתנה משוער: {wait_time} דקות.",
        "hold_music": "hold_music_hebrew.mp3",
        "periodic_message_he": "תודה שאתם ממתינים. שיחתכם חשובה לנו.",
        "periodic_interval_seconds": 60,
    },
}

Hebrew IVR Prompt Best Practices

Rule	Example	Why
Use formal register (second person plural)	"הקישו 1" not "תקיש 1"	Professional tone, avoids gender
Keep prompts under 15 seconds	3-4 options max per menu level	Callers lose patience quickly
Announce hours before after-hours message	"שעות הפעילות: א'-ה' 9-17"	Reduces callback attempts
Offer English option	"For English, press 9"	20% of Israeli calls may prefer English
Use "כוכבית" for star key	"לחזרה, הקישו כוכבית"	Standard Hebrew term for *
Use "סולמית" for hash/pound key	"לאישור, הקישו סולמית"	Standard Hebrew term for #
Repeat the menu on timeout	After 8 seconds of no input	Callers may need time to listen
Provide voicemail option after hours	"להשאיר הודעה, הקישו 1"	Captures leads outside business hours

Step 5: Voicemail-to-Text Transcription Pipeline

import os
import json
from datetime import datetime

def process_voicemail(audio_path: str, caller_number: str) -> dict:
    """
    Process a voicemail recording: transcribe, classify, and route.

    Args:
        audio_path: Path to the voicemail audio file
        caller_number: Caller's phone number (+972...)

    Returns:
        Processed voicemail with transcript and routing info
    """
    # Step 1: Transcribe using Whisper (best Hebrew accuracy)
    transcript = transcribe_hebrew(audio_path)

    # Step 2: Detect language (Hebrew, English, or mixed)
    language = detect_voicemail_language(transcript)

    # Step 3: Classify intent
    intent = classify_voicemail_intent(transcript)

    # Step 4: Extract key entities
    entities = extract_voicemail_entities(transcript)

    result = {
        "caller": caller_number,
        "timestamp": datetime.now().isoformat(),
        "transcript": transcript,
        "language": language,
        "intent": intent,
        "entities": entities,
        "audio_path": audio_path,
        "duration_seconds": get_audio_duration(audio_path),
    }

    # Step 5: Route based on intent
    result["routing"] = route_voicemail(intent, entities)

    return result


def detect_voicemail_language(text: str) -> str:
    """Detect whether voicemail is Hebrew, English, or mixed."""
    hebrew_chars = sum(1 for c in text if "\u0590" <= c <= "\u05FF")
    latin_chars = sum(1 for c in text if c.isascii() and c.isalpha())
    total = hebrew_chars + latin_chars

    if total == 0:
        return "unknown"

    hebrew_ratio = hebrew_chars / total

    if hebrew_ratio > 0.7:
        return "hebrew"
    elif hebrew_ratio < 0.3:
        return "english"
    else:
        return "mixed"


VOICEMAIL_INTENTS = {
    "callback_request": ["תתקשרו", "תחזרו", "חזרו אליי", "תתקשר"],
    "order_inquiry": ["הזמנה", "משלוח", "חבילה", "מעקב"],
    "complaint": ["תלונה", "בעיה", "לא מרוצה", "לא עובד"],
    "appointment": ["תור", "פגישה", "לקבוע", "לתאם"],
    "general": [],
}


def classify_voicemail_intent(transcript: str) -> str:
    """Classify voicemail intent based on Hebrew keywords."""
    for intent, keywords in VOICEMAIL_INTENTS.items():
        if any(keyword in transcript for keyword in keywords):
            return intent
    return "general"

Step 6: Mixed Language Handling (Hebrew-English)

Israeli tech professionals frequently switch between Hebrew and English mid-sentence (code-switching). Voice bots must handle this gracefully.

def handle_mixed_speech(audio_path: str) -> dict:
    """
    Handle mixed Hebrew-English speech common in Israeli tech.

    Strategy: Use Whisper without language hint for auto-detection,
    then post-process to normalize mixed output.
    """
    client = openai.OpenAI()

    with open(audio_path, "rb") as f:
        # Omit language parameter to let Whisper handle code-switching
        transcript = client.audio.transcriptions.create(
            model="whisper-1",
            file=f,
            response_format="verbose_json",
        )

    segments = []
    for segment in transcript.segments:
        text = segment["text"]
        lang = detect_segment_language(text)
        segments.append({
            "text": text,
            "language": lang,
            "start": segment["start"],
            "end": segment["end"],
        })

    return {
        "full_transcript": transcript.text,
        "segments": segments,
        "detected_languages": list(set(s["language"] for s in segments)),
    }


# Common Hebrew-English tech phrases that Whisper may mishandle
HEBREW_ENGLISH_CORRECTIONS = {
    "דיפלוי": "deploy",     # Hebrew-accented English
    "פושׁ": "push",
    "קומיט": "commit",
    "סרבר": "server",
    "באג": "bug",
    "פיצ'ר": "feature",
    "אפליקציה": "application",
    "דאטהבייס": "database",
}

Step 7: Phone Integration (Twilio)

Setting Up Twilio with Israeli Numbers (+972)

from twilio.rest import Client
from twilio.twiml.voice_response import VoiceResponse, Gather

TWILIO_ACCOUNT_SID = "YOUR_SID"
TWILIO_AUTH_TOKEN = "YOUR_TOKEN"

client = Client(TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN)


def purchase_israeli_number():
    """Purchase an Israeli phone number from Twilio."""
    numbers = client.available_phone_numbers("IL").local.list(limit=5)
    if numbers:
        purchased = client.incoming_phone_numbers.create(
            phone_number=numbers[0].phone_number,
            voice_url="https://your-server.com/voice/incoming",
            voice_method="POST",
        )
        return purchased.phone_number
    return None


# Flask webhook handler for incoming calls
from flask import Flask, request

app = Flask(__name__)


@app.route("/voice/incoming", methods=["POST"])
def handle_incoming_call():
    """Handle incoming call with Hebrew IVR menu."""
    response = VoiceResponse()

    # Welcome message in Hebrew
    response.say(
        "שלום, הגעתם לשירות הלקוחות.",
        language="he-IL",
        voice="Google.he-IL-Wavenet-A",
    )

    # Gather DTMF input with Hebrew prompt
    gather = Gather(
        num_digits=1,
        action="/voice/menu-selection",
        timeout=8,
        language="he-IL",
    )
    gather.say(
        "לשירות לקוחות, הקישו 1. למכירות, הקישו 2. לתמיכה טכנית, הקישו 3.",
        language="he-IL",
        voice="Google.he-IL-Wavenet-A",
    )
    response.append(gather)

    # If no input, repeat
    response.redirect("/voice/incoming")

    return str(response)


@app.route("/voice/menu-selection", methods=["POST"])
def handle_menu_selection():
    """Route based on DTMF selection."""
    digit = request.form.get("Digits", "")
    response = VoiceResponse()

    routes = {
        "1": "/voice/customer-service",
        "2": "/voice/sales",
        "3": "/voice/tech-support",
    }

    if digit in routes:
        response.redirect(routes[digit])
    else:
        response.say(
            "בחירה לא תקינה. בבקשה נסו שוב.",
            language="he-IL",
            voice="Google.he-IL-Wavenet-A",
        )
        response.redirect("/voice/incoming")

    return str(response)


@app.route("/voice/voicemail", methods=["POST"])
def handle_voicemail():
    """Record a voicemail with Hebrew instructions."""
    response = VoiceResponse()

    response.say(
        "אנחנו כרגע לא זמינים. בבקשה השאירו הודעה אחרי הצפצוף ונחזור אליכם בהקדם.",
        language="he-IL",
        voice="Google.he-IL-Wavenet-A",
    )

    response.record(
        max_length=120,  # 2 minutes max
        action="/voice/voicemail-complete",
        transcribe=False,  # We handle transcription ourselves for better Hebrew
        play_beep=True,
    )

    return str(response)

Step 8: Hebrew Accent Handling

Hebrew speakers in Israel have diverse accent backgrounds that affect speech recognition accuracy.

Accent Type	Characteristics	STT Impact
Standard Israeli	Modern Israeli pronunciation, merged alef/ayin, no distinction between chet/chaf	Baseline accuracy, all models handle well
Russian-accented	Hard "r" (guttural to alveolar), softer sibilants, vowel shifts	May reduce accuracy by 5-10%. Add Russian as alternate language hint
Arabic-accented	Preserved pharyngeal sounds (ayin, chet), emphatic consonants	Generally handled well by models trained on Israeli data
Ethiopian-accented	Distinct vowel patterns, different stress patterns	May need custom model training for high accuracy
English-accented	American/British vowel sounds applied to Hebrew, different "r"	Mixed results. Whisper handles best due to multilingual training

Improving accuracy for non-standard accents:

• Use Whisper as primary (trained on diverse accents)
• For Google/Azure, consider custom speech models with accent-specific training data
• Implement a confidence threshold: if STT confidence is below 0.7, ask the caller to repeat
• Add domain-specific vocabulary to improve recognition of industry terms

Run the demo script to test Hebrew STT with sample audio:

python scripts/hebrew-stt-demo.py --help

Examples

Example 1: Build a Restaurant Reservation IVR

User says: "I need an IVR system for a restaurant in Tel Aviv. Callers should be able to make reservations, check hours, and hear the menu."

Actions:

1. Design a 3-option main menu: reservations (1), hours/location (2), menu (3)
2. Set up business hours routing: Sunday-Thursday 11:00-23:00, Friday 11:00-15:00, Saturday closed
3. Configure Hebrew TTS for all prompts using Google Cloud Wavenet voices
4. Implement reservation flow: gather date, party size, name, phone confirmation
5. Set up after-hours voicemail with transcription pipeline
6. Integrate with Twilio using an Israeli +972 number

Result: Complete IVR system with Hebrew prompts, business-hours-aware routing, and voicemail transcription.

Example 2: Customer Service Voice Bot

User says: "Build a conversational voice bot for our e-commerce site. It should handle order status, returns, and escalate to a human agent."

Actions:

1. Set up Twilio webhook for incoming calls
2. Configure Google Cloud STT for real-time streaming transcription (he-IL, phone_call model)
3. Process transcribed text through an LLM for intent detection and response generation
4. Use Azure Neural TTS (he-IL-HilaNeural) for natural Hebrew responses
5. Implement order lookup by order number (DTMF or spoken digits)
6. Add human agent escalation with queue management
7. Handle mixed Hebrew-English input for product names

Result: Conversational voice bot that understands Hebrew speech, provides order information, and seamlessly escalates to human agents.

Example 3: Voicemail Transcription Service

User says: "I want to transcribe voicemails left on our business line and send them as text messages to the relevant department."

Actions:

1. Configure Twilio recording webhook to capture voicemail audio
2. Set up Whisper-based transcription pipeline for Hebrew
3. Classify voicemail intent (callback request, complaint, order inquiry)
4. Extract entities (phone numbers, order numbers, names)
5. Route transcribed text via SMS/WhatsApp to the relevant department
6. Store transcripts with audio links for reference

Result: Automated voicemail-to-text pipeline that transcribes Hebrew voicemails and routes them by intent.

Example 4: Handling Mixed Hebrew-English Speech

User says: "Our callers frequently mix Hebrew and English, especially tech terms. How do I handle this?"

Actions:

1. Configure Whisper without a fixed language parameter (auto-detection handles code-switching)
2. Implement post-processing to normalize Hebrew-accented English tech terms
3. Build a custom vocabulary of Hebrew-English tech terms (deploy, push, server, bug)
4. Test with sample mixed-language audio using the demo script
5. Set confidence thresholds and fallback to asking the caller to repeat if low

Result: Voice bot that correctly transcribes mixed Hebrew-English speech common in Israeli tech environments.

Bundled Resources

Scripts

• scripts/hebrew-stt-demo.py -- Demo script for Hebrew speech-to-text using OpenAI Whisper. Generates a sample Hebrew audio file using TTS and transcribes it back to text. Tests basic Hebrew STT accuracy. Run: python scripts/hebrew-stt-demo.py --help

References

• references/hebrew-stt-models.md -- Comparison table of Hebrew speech-to-text models (Whisper, Google Cloud STT, Azure Speech) with accuracy benchmarks, latency, pricing, and recommendations by use case. Consult when choosing an STT provider.
• references/ivr-design-patterns.md -- Common IVR flow patterns for Israeli businesses including restaurant, clinic, customer service, and government office templates. Consult when designing IVR menu structures.

Gotchas

• Hebrew speech-to-text engines struggle with Israeli slang ("yalla", "sababa", "balagan") and loan words from Arabic, Russian, and Amharic. Agents may not account for multilingual input in Hebrew voice bots.
• Israeli phone IVR systems must offer Hebrew as the default language, with English as secondary. Agents may build voice bots with English as the default, frustrating Hebrew-speaking callers.
• Hebrew Text-to-Speech (TTS) requires correct nikud (vowel diacritics) placement for proper pronunciation. Without nikud, ambiguous words like "דבר" may be read as "davar" (thing) or "daber" (speak).
• Israeli phone numbers have varying IVR input lengths: landlines are 9 digits (0X-XXXXXXX), mobile are 10 digits (05X-XXXXXXX). Voice bots must accept both formats.
• Background noise levels in typical Israeli environments (cafes, open offices, public transit) are higher than global averages. Voice bot confidence thresholds should be set lower to avoid excessive re-prompts.
• ElevenLabs eleven_v3 is REST-only, no WebSocket / streaming API as of May 2026. Agents that pick v3 for "best Hebrew quality" and then try to wire it into a live voice agent will hit unacceptable latency. Use OpenAI Realtime API, Inworld Realtime TTS-2, or Deepdub Phantom X 3.2 for sub-second turn-taking in Hebrew; reserve v3 for offline / batch / playback scenarios.
• The Hebrew TTS landscape moves fast (Deepdub Phantom X shipped March 2026, Inworld TTS-1.5 January 2026, Realtime TTS-2 later in 2026). Re-verify provider Hebrew support at build time rather than trusting any list older than a few months.

Reference Links

Source	URL	What to Check
OpenAI Whisper (Hebrew STT)	https://github.com/openai/whisper	Multilingual speech-to-text including Hebrew, model sizes, accuracy benchmarks
Google Cloud Speech-to-Text	https://cloud.google.com/speech-to-text/docs/languages	Hebrew (he-IL) support, streaming recognition, pricing
Azure AI Speech (Hebrew)	https://learn.microsoft.com/en-us/azure/ai-services/speech-service/language-support	Hebrew STT/TTS voices, neural voice list
HuggingFace Hebrew models	https://huggingface.co/models?language=he	Open Hebrew ASR/TTS models (ivrit.ai, etc.)
ivrit.ai (Hebrew voice corpus)	https://www.ivrit.ai	Open-source Hebrew speech corpus, pre-trained ASR models
ElevenLabs WebSocket docs	https://elevenlabs.io/docs/developers/websockets	Which models support streaming (v3 does NOT, Flash v2.5 does)
Deepdub (Israeli)	https://deepdub.ai	Phantom X 3.2 real-time AI voice with native Hebrew, emotive eTTS
Inworld TTS	https://inworld.ai/tts	TTS-1.5 (15 languages incl. Hebrew) + Realtime TTS-2 (100+ languages, sub-130ms)

Troubleshooting

Error: "Hebrew transcription returns Arabic text"

Cause: STT model misidentifies Hebrew as Arabic due to shared character ranges or similar phonemes. Solution: Explicitly set the language to "he-IL" (Google/Azure) or language="he" (Whisper). For Whisper, adding a Hebrew prompt hint also helps: prompt="שלום, ברוכים הבאים".

Error: "TTS voice sounds robotic for Hebrew"

Cause: Using Standard-tier voices instead of Neural/Wavenet voices. Solution: Switch to neural voices: Google Wavenet (he-IL-Wavenet-A/B) or Azure Neural (he-IL-HilaNeural). Amazon Polly Hebrew only offers a standard voice (Avri, male) with no neural option. Neural voices are more expensive but significantly more natural.

Error: "IVR menu times out before caller responds"

Cause: Timeout too short, especially for elderly callers or long Hebrew prompts. Solution: Increase gather timeout to 8-10 seconds. Add a "repeat" option ("לשמוע שוב, הקישו כוכבית"). Consider that Hebrew prompts may take longer than English due to longer word counts for the same content.

Error: "Twilio cannot find Israeli numbers"

Cause: Israeli number availability varies. Twilio has limited +972 inventory compared to US numbers. Solution: Search for both local and toll-free numbers. Consider Vonage as an alternative with better Israeli number availability. For high-volume needs, contact Twilio sales for dedicated number blocks. You can also port existing Israeli numbers to Twilio.