ASR Adaptation for Multi-Program Broadcast Audio
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems face significant accuracy drops when handling audio streams with multiple audio programs and multiple speakers, as they struggle to adapt to changes in genres and speakers, particularly in dynamic environments like broadcast media.
Innovation Solution
The solution involves deriving identifiers for audio segments to generate adaptation parameters for acoustic and language models, allowing the ASR system to adapt on the fly by selecting suitable parameters for each segment, such as speaker-specific and domain-specific models, to improve transcription accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speaker independent ASR systems are used to allow multiple users, then flexibility and ease of operation improve, but transcription accuracy deteriorates
Solution Approach 1:
The system dynamically adapts the acoustic model by continuously updating speaker-specific parameters during operation. The ASR system transitions from a static speaker-independent model to a dynamic speaker-adaptive model that evolves as it processes audio segments, allowing it to maintain flexibility while improving accuracy over time through online adaptation.
Solution Approach 2:
The system changes the parameters of the acoustic model based on detected speaker characteristics. By extracting speaker-specific features from audio segments and adjusting model parameters accordingly, the system maintains the convenience of speaker-independent operation while achieving speaker-dependent accuracy through parameter adaptation.
2Measurement precision
If speaker dependent ASR systems are used to improve transcription accuracy, then measurement precision improves, but adaptability and ease of operation worsen
Solution Approach 1:
The system creates a universal ASR framework that can function in both speaker-independent and speaker-dependent modes. By implementing online adaptation capabilities, a single system serves multiple functions: it operates as a general-purpose speaker-independent system initially, then adapts to become speaker-specific when needed, eliminating the need for separate systems for different user scenarios.
Solution Approach 2:
The ASR system performs self-adaptation by automatically extracting speaker characteristics from audio segments and updating its acoustic model without external intervention. The system serves itself by autonomously improving its performance for each speaker it encounters, removing the need for manual enrollment or configuration procedures.
3Measurement precision
If the ASR system adapts to each speaker and audio program, then transcription accuracy improves, but device complexity increases
Solution Approach 1:
The system segments the audio stream into distinct audio segments and processes each segment with appropriate speaker-specific parameters. By dividing the continuous adaptation task into discrete segment-level operations, the system manages complexity through modular processing while achieving accurate transcription for each segment through targeted adaptation.
Solution Approach 2:
The system applies partial adaptation by updating only the necessary speaker-specific parameters of the acoustic model rather than retraining the entire model. This selective adaptation approach achieves the needed accuracy improvement while avoiding the computational burden and complexity of complete model retraining, applying just enough adaptation to solve the problem at hand.
Data Source
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AI summary
AM and LM parameters to be used for adapting an ASR model are derived for each audio segment of an audio stream comprising multiple audio programs. A set of identifiers, including a speaker identifier, a speaker domain identifier and a program domain identifier, is obtained for each audio segment. The set of identifiers are used to select most suitable AM and LM parameters for the particular audio segment. 5 The embodiments enable provision of maximum constraints on the AMs and LMs and enable adaptation of the ASR model on the fly for audio streams of multiple audio programs, such as broadcast audio. This means that the embodiments enable selecting AM and LM parameters that are most suitable in terms of ASR performance for each audio segment.