Search Results for author:
Found 20 papers, 7 papers with code
Word Level Timestamp Generation for Automatic Speech Recognition and Translation
We introduce a data-driven approach for enabling word-level timestamp prediction in the Canary model.
Automatic Speech Recognition
automatic-speech-translation
+4
Training and Inference Efficiency of Encoder-Decoder Speech Models
However, the reported data and compute requirements for their training are prohibitive for many in the research community.
Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks
We present Dynamic-SUPERB Phase-2, an open and evolving benchmark for the comprehensive evaluation of instruction-based universal speech models.
VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning
More recent studies have extended this to multi-turn conversations, though they often require complex, multi-stage supervised fine-tuning (SFT) with diverse data.
META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR
We propose a novel end-to-end multi-talker automatic speech recognition (ASR) framework that enables both multi-speaker (MS) ASR and target-speaker (TS) ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition
Given recent advances in generative AI technology, a key question is how large language models (LLMs) can enhance acoustic modeling tasks using text decoding results from a frozen, pretrained automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Sortformer: Seamless Integration of Speaker Diarization and ASR by Bridging Timestamps and Tokens
We demonstrate that combining Sort Loss and PIL achieves performance competitive with state-of-the-art end-to-end diarization models trained exclusively with PIL.
Resource-Efficient Adaptation of Speech Foundation Models for Multi-Speaker ASR
Speech foundation models have achieved state-of-the-art (SoTA) performance across various tasks, such as automatic speech recognition (ASR) in hundreds of languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations
Discrete speech representations have garnered recent attention for their efficacy in training transformer-based models for various speech-related tasks such as automatic speech recognition (ASR), translation, speaker verification, and joint speech-text foundational models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Less is More: Accurate Speech Recognition & Translation without Web-Scale Data
Recent advances in speech recognition and translation rely on hundreds of thousands of hours of Internet speech data.
Spectral Codecs: Spectrogram-Based Audio Codecs for High Quality Speech Synthesis
Recently, discrete audio tokens produced by neural audio codecs have become a popular alternate speech representation for speech synthesis tasks such as text-to-speech (TTS).
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.
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.
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.
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.
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
