US 2021/0343274 A1 · Fast and Robust Unsupervised Contextual Biasing for Speech Recognition

Abstract

An automatic speech recognition (ASR) system that determines a textual representation of a word from a word spoken in a natural language is provided. The ASR system uses an acoustic model, a language model, and a decoder. When the ASR system receives a spoken word, the acoustic model generates word candidates for the spoken word. The language model determines an n-gram score for each word candidate. The n-gram score includes a base score and a bias score. The bias score is based on a logarithmic probability of the word candidate, where the logarithmic probability is derived using a class-based language model where the words are clustered into non-overlapping clusters according to word statistics. The decoder decodes a textual representation of the spoken word from the word candidates and the corresponding n-gram score for each word candidate.

Claims21

Dependent claims

An automatic speech recognition (ASR) system that determines a textual representation of a word spoken in a natural language, the system comprising:

a language model configured to:
for each word candidate in word candidates that correspond to a spoken word:
determine a base score;
determine a bias score from a logarithmic probability associated with the each word candidate, wherein the logarithmic probability for the each word candidate is determined from a class-based language model;
and
combine the base score and the bias score into an n-gram score;
and
a decoder neural network configured to determine the textual representation of the spoken word using n-gram scores associated with the word candidates.

5 more elements

A method for determining a textual representation of a word spoken in a natural language, the method comprising:

for each word candidate in word candidates that correspond to a spoken word:
determining, using a language model stored in memory and executed on a processor, a base score;
determine, using the language model, a bias score from a logarithmic probability associated with the each word candidate, wherein the logarithmic probability for the each word candidate is pre-determined using a class-based language model;
and
combining the base score and the bias score into an n-gram score;
and
determining, using a decoder neural network, the textual representation of the spoken word using n-gram scores associated with the word candidates.

4 more elements

Dependencies are inferred from the claim text.

Fast and Robust Unsupervised Contextual Biasing for Speech Recognition

Life of this patent

Fast and Robust Unsupervised Contextual Biasing for Speech Recognition

Life of this patent

Abstract

Claims21