针对随机词汇请求，能否在Azure Speech To Text API中传入预期词汇列表？

Great question! I’ve run into this exact scenario before, and Azure Speech to Text does have a solution that avoids the hassle of creating custom models for one-off, random vocabulary lists—meet Phrase Lists.

What are Phrase Lists? #

Phrase Lists let you dynamically inject specific words, phrases, or terms directly into your API request (or speech recognition session) without needing to train a custom model. The Azure recognition engine prioritizes these terms, boosting their recognition accuracy—perfect for your use case where vocabulary changes randomly with each request.

How to Use Phrase Lists (Example) #

Here’s a quick Python SDK example showing how to add custom phrases to your recognition request on the fly:

import azure.cognitiveservices.speech as speechsdk

# Initialize core configs
speech_config = speechsdk.SpeechConfig(subscription="YOUR_SUBSCRIPTION_KEY", region="YOUR_REGION")
audio_config = speechsdk.audio.AudioConfig(filename="your_audio_input.wav")

# Create recognizer instance
speech_recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config, audio_config=audio_config)

# Add your dynamic expected vocabulary to the phrase list
phrase_list = speechsdk.PhraseListGrammar.from_recognizer(speech_recognizer)
phrase_list.addPhrase("custom term 1")
phrase_list.addPhrase("uncommon phrase")
phrase_list.addPhrase("random jargon you need recognized")

# Execute recognition
result = speech_recognizer.recognize_once()

# Handle the result
if result.reason == speechsdk.ResultReason.RecognizedSpeech:
    print(f"Recognized text: {result.text}")
elif result.reason == speechsdk.ResultReason.NoMatch:
    print(f"No speech detected: {result.no_match_details}")
elif result.reason == speechsdk.ResultReason.Canceled:
    print(f"Recognition canceled: {result.cancellation_details.reason}")
    if result.cancellation_details.reason == speechsdk.CancellationReason.Error:
        print(f"Error details: {result.cancellation_details.error_details}")

Key Benefits for Your Scenario #

• No model training required: Unlike custom speech models, you don’t need to pre-process or train anything—just add phrases at request time.
• Dynamic flexibility: Update the phrase list for every new request to match your random vocabulary.
• Scalable: You can add up to 1000 phrases per list, which covers most ad-hoc use cases.

This feature aligns perfectly with the functionality you’re used to with Google Speech to Text’s expected vocabulary lists, eliminating the need for the tedious custom model workflow you described.

内容的提问来源于stack exchange，提问作者Harry Stuart