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Dasb-discrete audio and speech benchmark
Discrete audio tokens have recently gained considerable attention for their potential to
connect audio and language processing, enabling the creation of modern multimodal large …
connect audio and language processing, enabling the creation of modern multimodal large …
Self-supervised speech representations are more phonetic than semantic
Self-supervised speech models (S3Ms) have become an effective backbone for speech
applications. Various analyses suggest that S3Ms encode linguistic properties. In this work …
applications. Various analyses suggest that S3Ms encode linguistic properties. In this work …
The Interspeech 2024 challenge on speech processing using discrete units
Representing speech and audio signals in discrete units has become a compelling
alternative to traditional high-dimensional feature vectors. Numerous studies have …
alternative to traditional high-dimensional feature vectors. Numerous studies have …
Espnet-codec: Comprehensive training and evaluation of neural codecs for audio, music, and speech
Neural codecs have become crucial to recent speech and audio generation research. In
addition to signal compression capabilities, discrete codecs have also been found to …
addition to signal compression capabilities, discrete codecs have also been found to …
MMM: Multi-layer multi-residual multi-stream discrete speech representation from self-supervised learning model
Speech discrete representation has proven effective in various downstream applications
due to its superior compression rate of the waveform, fast convergence during training, and …
due to its superior compression rate of the waveform, fast convergence during training, and …
How should we extract discrete audio tokens from self-supervised models?
Discrete audio tokens have recently gained attention for their potential to bridge the gap
between audio and language processing. Ideal audio tokens must preserve content …
between audio and language processing. Ideal audio tokens must preserve content …
DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding
The integration of pre-trained text-based large language models (LLM) with speech input
has enabled instruction-following capabilities for diverse speech tasks. This integration …
has enabled instruction-following capabilities for diverse speech tasks. This integration …
mhubert-147: A compact multilingual hubert model
We present mHuBERT-147, the first general-purpose massively multilingual HuBERT
speech representation model trained on 90K hours of clean, open-license data. To scale up …
speech representation model trained on 90K hours of clean, open-license data. To scale up …
Scaling properties of speech language models
Speech Language Models (SLMs) aim to learn language from raw audio, without textual
resources. Despite significant advances, our current models exhibit weak syntax and …
resources. Despite significant advances, our current models exhibit weak syntax and …
Speechprompt: Prompting speech language models for speech processing tasks
Prompting has become a practical method for utilizing pre-trained language models (LMs).
This approach offers several advantages. It allows an LM to adapt to new tasks with minimal …
This approach offers several advantages. It allows an LM to adapt to new tasks with minimal …