Custom Vocabulary
Custom vocabulary improve speech recognition accuracy by biasing the speech engine towards specific words and phrases that you specify.
Use custom vocabulary to achieve higher accuracy in recognizing:
- Proper nouns: product names, people's names (e.g. "Soniox", "Klemen")
- Domain-specific words: medical terms, legal jargon (e.g. "zestoretic", "ombudsman")
- Ambiguous words: same-sounding words (e.g. "Carrie" vs "Kerry")
Introduction
Custom vocabulary is specified with the SpeechContext
object as part of a transcription request.
It contains a list of SpeechContextEntry
objects, each entry specifying a list of phrases
(which can be words or phrases) and a boost
value.
The effects of specifying the SpeechContext
are:
- Ensures all the specified words and phrases are in the system's vocabulary. This helps with recognizing out-of-vocabulary words.
- The system applies the bias specified by the
boost
parameter to the associated words or phrases.
Boost Parameter
The boost
parameter specifies the amount of bias the speech system assigns to a particular word or phrase. The boost parameter can be positive or negative, i.e. to make the speech model more or less likely to recognize the words or phrases. The allowed range of values for the boost parameter is between -30 to +30. If the boost
parameter is not specified, no bias is assigned.
If you assign a boost
value to a multi-word phrase, boost is applied to the phase in its entirety. For example, by assigning a boost value to the multi-word phrase "this speech recognition works", will result in more likely recognition of this entire phrase and not the individual words within the phrase. The maximum number of words permitted in a phrase is 5.
We recommend setting boost
to 10 to start with and experimenting by adjusting the value for optimal results.
Single Word
In this example, we specify and boost acetylcarnitine and zestoretic words, which should result in significanly more accurate recognition of the two words.
# Create SpeechContext.
speech_context = SpeechContext(
entries=[
SpeechContextEntry(
phrases=["acetylcarnitine"],
boost=20,
),
SpeechContextEntry(
phrases=["zestoretic"],
boost=20,
)
]
)
# Pass SpeechContext with transcribe request.
result = transcribe_file_short(
"../test_data/acetylcarnitine_zestoretic.flac",
client,
speech_context=speech_context
)
Run
python3 customization_single_word.py
Output
Acetylcarnitine is a molecule and zestoretic is a medication .
// Create SpeechContext.
const speech_context = {
entries: [
{
phrases: ["acetylcarnitine"],
boost: 20,
},
{
phrases: ["zestoretic"],
boost: 20,
}
]
};
// Pass SpeechContext with transcribe request.
const result = await speechClient.transcribeFileShort(
"../test_data/acetylcarnitine_zestoretic.flac",
{ speech_context: speech_context }
);
Run
node customization_single_word.js
Output
Acetylcarnitine is a molecule and zestoretic is a medication .
Multi-Word Phrases
When specifying phrases, you can use custom vocabulary to resolve ambiguity in recognizing accoustically similar sounding words.
# Create SpeechContext.
speech_context = SpeechContext(
entries=[
SpeechContextEntry(
phrases=["carrie underwood", "kerry washington"],
boost=10,
)
]
)
# Pass SpeechContext with transcribe request.
result = transcribe_file_short(
"../test_data/carrie_underwood_kerry_washington.flac",
client,
speech_context=speech_context
)
Run
python3 customization_multi_word.py
Output
Carry Underwood and Kerry Washington
// Create SpeechContext.
const speech_context = {
entries: [
{
phrases: ["carrie underwood", "kerry washington"],
boost: 10,
}
]
};
// Pass SpeechContext with transcribe request.
const result = await speechClient.transcribeFileShort(
"../test_data/carrie_underwood_kerry_washington.flac",
{ speech_context: speech_context }
);
Run
node customization_multi_word.js
Output
Carry Underwood and Kerry Washington
Expanding Vocabulary
Recognizing new words or out-of-vocabulary words is automatically supported with custom vocabulary. In this example, speechology is a made-up word. However, we can easily enable the system to recognize this word by adding it to the SpeechContext
with a sufficiently high boost
value.
# Create SpeechContext.
speech_context = SpeechContext(
entries=[
SpeechContextEntry(
phrases=["speechology"],
boost=10,
)
]
)
# Pass SpeechContext with transcribe request.
result = transcribe_file_short(
"../test_data/speechology.flac", client, speech_context=speech_context
)
Run
python3 customization_new_word.py
Output
Speechology is a made up word
// Create SpeechContext.
const speech_context = {
entries: [
{
phrases: ["speechology"],
boost: 10,
}
]
};
// Pass SpeechContext with transcribe request.
const result = await speechClient.transcribeFileShort(
"../test_data/speechology.flac",
{ speech_context: speech_context }
);
Run
node customization_new_word.js
Output
Speechology is a made up word
Manage Vocabularies
To learn how to reuse an existing vocabulary and create multiple vocabularies, see Manage Vocabularies.