Conformer-Based Speech Conversion With Typical-Speech Pretraining
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Solution Overview
Problem
Current speech conversion models struggle to effectively train on insufficient data from speakers with speech impairments, making it difficult to convert atypical speech into canonical fluent speech, especially for conditions like ALS, heavily-accented speech, and deaf speech, due to the challenges in obtaining sufficient training data and maintaining optimal performance.
Innovation Solution
A Conformer-based speech conversion model with an encoder and decoder architecture that includes subsampling and upsampling layers, allowing for less data-intensive training and robust conversion of atypical speech into canonical fluent speech, using a two-step training process to pre-train on typical speakers and fine-tune for atypical speakers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional speech conversion models are used to convert atypical speech into canonical fluent speech, then the conversion capability is provided, but the models require extensive training data from speakers with speech impairments which is insufficient and difficult to obtain
Solution Approach 1:
The model is pre-trained on abundant data from typical speakers before being fine-tuned on limited atypical speech data. This preliminary training establishes a strong foundation that enables effective learning from scarce atypical speech examples, resolving the contradiction between needing reliable conversion capability and lacking sufficient training data.
Solution Approach 2:
The model employs frame rate conversion and temporal downsampling to change the temporal parameters of speech processing. By processing speech at different frame rates and then converting back, the model learns robust representations that generalize better from limited atypical speech data, improving conversion reliability without requiring more training data.
2Measurement precision
If more training data from atypical speakers is collected to improve conversion accuracy, then the speech conversion quality improves, but the complexity of data collection and processing increases
Solution Approach 1:
By pre-training on typical speech data before fine-tuning on atypical speech, the model reduces the amount of atypical speech data needed for effective training. This two-stage approach simplifies the overall data collection process while maintaining high conversion accuracy.
Solution Approach 2:
The model uses an intermediate representation space created during pre-training that bridges typical and atypical speech. This intermediate representation acts as a mediator that enables effective learning from limited atypical speech data without requiring complex data processing pipelines.
3Manufacturing precision
If the model processes speech at high frame rates to maintain temporal resolution, then the speech quality is preserved, but the computational complexity and training data requirements increase
Solution Approach 1:
The model processes speech at varying frame rates, using lower frame rates during certain processing stages and higher frame rates at others. This periodic variation in processing intensity maintains speech quality where needed while reducing computational complexity overall, avoiding the need for consistently high-rate processing.
Solution Approach 2:
The model dynamically changes temporal parameters through frame rate conversion and temporal downsampling. By adjusting the temporal resolution parameter at different processing stages, the model maintains speech quality when necessary while reducing computational complexity during other stages.
Data Source
AI summary
A method for speech conversion includes receiving, as input to an encoder of a speech conversion model, an input spectrogram corresponding to an utterance, the encoder including a stack of self-attention blocks. The method further includes generating, as output from the encoder, an encoded spectrogram and receiving, as input to a spectrogram decoder of the speech conversion model, the encoded spectrogram generated as output from the encoder. The method further includes generating, as output from the spectrogram decoder, an output spectrogram corresponding to a synthesized speech representation of the utterance.