Self-Training With Oracle And Top-Ranked Hypotheses

Organization Name

Inventor(s)

Murali Karthick Baskar of Mountain View CA (US)

Bhuvana Ramabhadran of Mt. Kisco NY (US)

Self-Training With Oracle And Top-Ranked Hypotheses - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240296832 titled 'Self-Training With Oracle And Top-Ranked Hypotheses

Simplified Explanation: The patent application describes a method that uses an rnn-t model to process acoustic frames for speech recognition, generating n-best lists of hypotheses and determining word errors relative to ground-truth transcriptions. It then calculates losses for the top-ranked hypothesis and an oracle hypothesis with the least errors, training the model based on a combined loss.

**Key Features and Innovation:**

   - Utilizes an rnn-t model for speech recognition.
   - Generates n-best lists of hypotheses for each training sample.
   - Determines word errors relative to ground-truth transcriptions.
   - Calculates losses for top-ranked and oracle hypotheses.
   - Trains the model based on a combined loss.

**Potential Applications:**

   - Speech recognition systems.
   - Language translation applications.
   - Voice-controlled devices.

**Problems Solved:**

   - Improving speech recognition accuracy.
   - Enhancing training methods for rnn-t models.
   - Reducing word errors in transcriptions.

**Benefits:**

   - Higher accuracy in speech recognition.
   - Enhanced training efficiency for models.
   - Improved performance in language-related tasks.

**Commercial Applications:**

   - Optimizing speech recognition software for businesses.
   - Enhancing voice-controlled products for consumers.
   - Improving language translation services for various industries.

**Questions about Speech Recognition Technology:**

   * **How does the rnn-t model improve speech recognition accuracy?**
       - The rnn-t model processes acoustic frames to generate accurate speech recognition hypotheses, leading to improved performance.
   * **What are the potential limitations of using n-best lists in speech recognition systems?**
       - N-best lists may increase computational complexity and require additional processing power, impacting real-time applications.

**Frequently Updated Research:**

   - Stay updated on advancements in rnn-t models for speech recognition.
   - Explore new techniques for reducing word errors in transcriptions.

Original Abstract Submitted

a method includes, for each training sample of a plurality of training samples, processing, using an rnn-t model, a corresponding sequence of acoustic frames to obtain an n-best list of speech recognition hypotheses, and, for each speech recognition hypothesis of the n-best list, determining a corresponding number of word errors relative to a corresponding ground-truth transcription. for a top-ranked hypothesis from the n-best list, the method includes determining a first loss based on the corresponding ground-truth transcription. the method includes identifying, as an oracle hypothesis, the speech recognition hypothesis from the n-best list having the smallest corresponding number of word errors relative to the corresponding ground-truth transcription, and determining a second loss for the oracle hypothesis based on the corresponding ground-truth transcription. the method includes determining a corresponding self-training combined loss based on the first and second losses, and training the model based on the corresponding self-training combined loss.