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Found 9 papers, 3 papers with code
The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System
We present the NVIDIA NeMo team's multi-channel speech recognition system for the 7th CHiME Challenge Distant Automatic Speech Recognition (DASR) Task, focusing on the development of a multi-channel, multi-speaker speech recognition system tailored to transcribe speech from distributed microphones and microphone arrays.
Property-Aware Multi-Speaker Data Simulation: A Probabilistic Modelling Technique for Synthetic Data Generation
This capability offers a tailored training environment for developing neural models suited for speaker diarization and voice activity detection.
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition
Discrete audio representation, aka audio tokenization, has seen renewed interest driven by its potential to facilitate the application of text language modeling approaches in audio domain.
Enhancing Speaker Diarization with Large Language Models: A Contextual Beam Search Approach
In addition, these findings point to the potential of using LLMs to improve speaker diarization and other speech processing tasks by capturing semantic and contextual cues.
Unified model for code-switching speech recognition and language identification based on a concatenated tokenizer
Code-Switching (CS) multilingual Automatic Speech Recognition (ASR) models can transcribe speech containing two or more alternating languages during a conversation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Phonetic Word Embeddings
This work presents a novel methodology for calculating the phonetic similarity between words taking motivation from the human perception of sounds.
Towards Adapting NMF Dictionaries Using Total Variability Modeling for Noise-Robust Acoustic Features
We propose an algorithm to extract noise-robust acoustic features from noisy speech.
Investigating Target Set Reduction for End-to-End Speech Recognition of Hindi-English Code-Switching Data
End-to-end (E2E) systems are fast replacing the conventional systems in the domain of automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Joint Language Identification of Code-Switching Speech using Attention based E2E Network
For the automatic recognition of code-switching speech, the conventional approaches often employ an LID system for detecting the languages present within an utterance.
