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Found 8 papers, 2 papers with code
WavLLM: Towards Robust and Adaptive Speech Large Language Model
In this work, we introduce WavLLM, a robust and adaptive speech large language model with dual encoders, and a prompt-aware LoRA weight adapter, optimized by a two-stage curriculum learning approach.
On decoder-only architecture for speech-to-text and large language model integration
Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language.
Code-Switching Text Generation and Injection in Mandarin-English ASR
Code-switching speech refers to a means of expression by mixing two or more languages within a single utterance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
FoundationTTS: Text-to-Speech for ASR Customization with Generative Language Model
Neural text-to-speech (TTS) generally consists of cascaded architecture with separately optimized acoustic model and vocoder, or end-to-end architecture with continuous mel-spectrograms or self-extracted speech frames as the intermediate representations to bridge acoustic model and vocoder, which suffers from two limitations: 1) the continuous acoustic frames are hard to predict with phoneme only, and acoustic information like duration or pitch is also needed to solve the one-to-many problem, which is not easy to scale on large scale and noise datasets; 2) to achieve diverse speech output based on continuous speech features, complex VAE or flow-based models are usually required.
Building High-accuracy Multilingual ASR with Gated Language Experts and Curriculum Training
We propose gated language experts and curriculum training to enhance multilingual transformer transducer models without requiring language identification (LID) input from users during inference.
Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition
The sparsely-gated Mixture of Experts (MoE) can magnify a network capacity with a little computational complexity.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition
Although multiple candidates are generated by an ASR system through beam search, current error correction approaches can only correct one sentence at a time, failing to leverage the voting effect from multiple candidates to better detect and correct error tokens.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition
A straightforward solution to reduce latency, inspired by non-autoregressive (NAR) neural machine translation, is to use an NAR sequence generation model for ASR error correction, which, however, comes at the cost of significantly increased ASR error rate.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
