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Found 14 papers, 3 papers with code
Stateful Conformer with Cache-based Inference for Streaming Automatic Speech Recognition
We also showed that training a model with multiple latencies can achieve better accuracy than single latency models while it enables us to support multiple latencies with a single model.
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.
SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation
We present a novel Speech Augmented Language Model (SALM) with {\em multitask} and {\em in-context} learning capabilities.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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.
Investigating End-to-End ASR Architectures for Long Form Audio Transcription
This paper presents an overview and evaluation of some of the end-to-end ASR models on long-form audios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling
We study speech intent classification and slot filling (SICSF) by proposing to use an encoder pretrained on speech recognition (ASR) to initialize an end-to-end (E2E) Conformer-Transformer model, which achieves the new state-of-the-art results on the SLURP dataset, with 90. 14% intent accuracy and 82. 27% SLURP-F1.
Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
Conformer-based models have become the dominant end-to-end architecture for speech processing tasks.
Ranked #1 on
Speech Recognition
on LibriSpeech test-other
A Compact End-to-End Model with Local and Global Context for Spoken Language Identification
We introduce TitaNet-LID, a compact end-to-end neural network for Spoken Language Identification (LID) that is based on the ContextNet architecture.
Multi-scale Speaker Diarization with Dynamic Scale Weighting
First, we use multi-scale clustering as an initialization to estimate the number of speakers and obtain the average speaker representation vector for each speaker and each scale.
CarneliNet: Neural Mixture Model for Automatic Speech Recognition
The basic idea is to introduce a parallel mixture of shallow networks instead of a very deep network.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition
We propose Citrinet - a new end-to-end convolutional Connectionist Temporal Classification (CTC) based automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition
In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters, and any necessary orthography (such as capitalization, punctuation, and denormalization of non-standard words) is imputed by separate post-processing models.
Ranked #3 on
Speech Recognition
on SPGISpeech
