18483492. EVALUATION-BASED SPEAKER CHANGE DETECTION EVALUATION METRICS simplified abstract (GOOGLE LLC)
Contents
- 1 EVALUATION-BASED SPEAKER CHANGE DETECTION EVALUATION METRICS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 EVALUATION-BASED SPEAKER CHANGE DETECTION EVALUATION METRICS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
EVALUATION-BASED SPEAKER CHANGE DETECTION EVALUATION METRICS
Organization Name
Inventor(s)
Guanlong Zhao of Long Island City NY (US)
Yiling Huang of Edgewater NJ (US)
Jason Pelecanos of Mountain View CA (US)
EVALUATION-BASED SPEAKER CHANGE DETECTION EVALUATION METRICS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18483492 titled 'EVALUATION-BASED SPEAKER CHANGE DETECTION EVALUATION METRICS
Simplified Explanation
The method involves processing audio data to predict speaker changes in a multi-utterance training sample, comparing these predictions to ground-truth speaker change intervals, and calculating a precision metric for the model based on the accuracy of the predictions.
- The method involves obtaining a training sample with audio data from multiple speakers.
- Ground-truth speaker change intervals are obtained to indicate when speaker changes occur in the audio data.
- A sequence transduction model is used to predict speaker changes in the audio data.
- Predicted speaker changes are compared to ground-truth intervals to determine accuracy.
- A precision metric is calculated based on the number of correct predictions out of the total number of predictions.
Potential Applications
This technology could be applied in various fields such as speech recognition, speaker diarization, and audio transcription.
Problems Solved
This technology helps in accurately identifying speaker changes in audio data, which can improve the performance of speech recognition systems and enhance the quality of audio transcription.
Benefits
The method provides a way to evaluate the performance of a sequence transduction model in predicting speaker changes, leading to potential improvements in speech processing applications.
Potential Commercial Applications
"Enhancing Speech Recognition Systems with Speaker Change Prediction Technology"
Possible Prior Art
One possible prior art could be the use of Hidden Markov Models (HMMs) in speaker diarization systems to detect speaker changes in audio data.
Unanswered Questions
=== How does this method compare to other speaker change detection techniques in terms of accuracy and efficiency? This article does not provide a direct comparison with other speaker change detection techniques, so it is unclear how this method performs in relation to existing methods.
=== What are the limitations of this technology in real-world applications? The article does not address the potential limitations or challenges of implementing this technology in practical settings, leaving room for further exploration into its feasibility and scalability.
Original Abstract Submitted
A method includes obtaining a multi-utterance training sample that includes audio data characterizing utterances spoken by two or more different speakers and obtaining ground-truth speaker change intervals indicating time intervals in the audio data where speaker changes among the two or more different speakers occur. The method also includes processing the audio data to generate a sequence of predicted speaker change tokens using a sequence transduction model. For each corresponding predicted speaker change token, the method includes labeling the corresponding predicted speaker change token as correct when the predicted speaker change token overlaps with one of the ground-truth speaker change intervals. The method also includes determining a precision metric of the sequence transduction model based on a number of the predicted speaker change tokens labeled as correct and a total number of the predicted speaker change tokens in the sequence of predicted speaker change tokens.
