An audio-visual language model that adds full-face visual features to a pre-trained expressive speech model improves emotion recognition and expressive speech generation by a few F1 points over speech-only on synthetic IEMOCAP data.
LMCodec: A Low Bitrate Speech Codec With Causal Transformer Models
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abstract
We introduce LMCodec, a causal neural speech codec that provides high quality audio at very low bitrates. The backbone of the system is a causal convolutional codec that encodes audio into a hierarchy of coarse-to-fine tokens using residual vector quantization. LMCodec trains a Transformer language model to predict the fine tokens from the coarse ones in a generative fashion, allowing for the transmission of fewer codes. A second Transformer predicts the uncertainty of the next codes given the past transmitted codes, and is used to perform conditional entropy coding. A MUSHRA subjective test was conducted and shows that the quality is comparable to reference codecs at higher bitrates. Example audio is available at https://mjenrungrot.github.io/chrome-media-audio-papers/publications/lmcodec.
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Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation
An audio-visual language model that adds full-face visual features to a pre-trained expressive speech model improves emotion recognition and expressive speech generation by a few F1 points over speech-only on synthetic IEMOCAP data.