4 citations · 13 across the 15 of their papers we have counts for
8 papers · 1 filter
Streaming Speech-to-Text Translation with a SpeechLLM
Titouan Parcollet, Shucong Zhang, Xianrui Zheng +1
Normally, a system that translates speech into text consists of separate modules for speech recognition and text-to-text translation. Combining those tasks into a SpeechLLM promise…
Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Use
Titouan Parcollet, Yuan Tseng, Shucong Zhang +1
Automatic speech recognition (ASR) research is driven by the availability of common datasets between industrial researchers and academics, encouraging comparisons and evaluations.…
Benchmarking Rotary Position Embeddings for Automatic Speech Recognition
Shucong Zhang, Titouan Parcollet, Rogier van Dalen +1
Self-attention relies on positional embeddings to encode input order. Relative Position (RelPos) embeddings are widely used in Automatic Speech Recognition (ASR). However, RelPos h…
Linear-Complexity Self-Supervised Learning for Speech Processing
Shucong Zhang, Titouan Parcollet, Rogier van Dalen +1
Self-supervised learning (SSL) models usually require weeks of pre-training with dozens of high-end GPUs. These models typically have a multi-headed self-attention (MHSA) context e…
LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech
Titouan Parcollet, Ha Nguyen, Solene Evain +19
Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing. Speech processing…
SummaryMixing: A Linear-Complexity Alternative to Self-Attention for Speech Recognition and Understanding
Titouan Parcollet, Rogier van Dalen, Shucong Zhang +1
Modern speech processing systems rely on self-attention. Unfortunately, token mixing with self-attention takes quadratic time in the length of the speech utterance, slowing down in…
On the Usefulness of Self-Attention for Automatic Speech Recognition with Transformers
Shucong Zhang, Erfan Loweimi, Peter Bell +1
Self-attention models such as Transformers, which can capture temporal relationships without being limited by the distance between events, have given competitive speech recognition…
Stochastic Attention Head Removal: A simple and effective method for improving Transformer Based ASR Models
Shucong Zhang, Erfan Loweimi, Peter Bell +1
Recently, Transformer based models have shown competitive automatic speech recognition (ASR) performance. One key factor in the success of these models is the multi-head attention…