Search Results for author:
Found 20 papers, 4 papers with code
Towards General-Purpose Text-Instruction-Guided Voice Conversion
This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice".
Text Injection for Capitalization and Turn-Taking Prediction in Speech Models
Text injection for automatic speech recognition (ASR), wherein unpaired text-only data is used to supplement paired audio-text data, has shown promising improvements for word error rate.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR
We address this limitation by distilling punctuation knowledge from a bidirectional teacher language model (LM) trained on written, punctuated text.
UML: A Universal Monolingual Output Layer for Multilingual ASR
Consequently, the UML enables to switch in the interpretation of each output node depending on the language of the input speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
E2E Segmentation in a Two-Pass Cascaded Encoder ASR Model
We explore unifying a neural segmenter with two-pass cascaded encoder ASR into a single model.
Unified End-to-End Speech Recognition and Endpointing for Fast and Efficient Speech Systems
In this work, we propose a method to jointly train the ASR and EP tasks in a single end-to-end (E2E) multitask model, improving EP quality by optionally leveraging information from the ASR audio encoder.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Streaming End-to-End Multilingual Speech Recognition with Joint Language Identification
Language identification is critical for many downstream tasks in automatic speech recognition (ASR), and is beneficial to integrate into multilingual end-to-end ASR as an additional task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Language Agnostic Multilingual Streaming On-Device ASR System
On-device end-to-end (E2E) models have shown improvements over a conventional model on English Voice Search tasks in both quality and latency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Streaming Intended Query Detection using E2E Modeling for Continued Conversation
In voice-enabled applications, a predetermined hotword isusually used to activate a device in order to attend to the query. However, speaking queries followed by a hotword each timeintroduces a cognitive burden in continued conversations.
Turn-Taking Prediction for Natural Conversational Speech
This makes doing speech recognition with conversational speech, including one with multiple queries, a challenging task.
E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR
Improving the performance of end-to-end ASR models on long utterances ranging from minutes to hours in length is an ongoing challenge in speech recognition.
Improving the fusion of acoustic and text representations in RNN-T
The recurrent neural network transducer (RNN-T) has recently become the mainstream end-to-end approach for streaming automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Better and Faster End-to-End Model for Streaming ASR
To address this, we explore replacing the LSTM layers in the encoder of our E2E model with Conformer layers [4], which has shown good improvements for ASR.
Audio and Speech Processing Sound
FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization
FastEmit also improves streaming ASR accuracy from 4. 4%/8. 9% to 3. 1%/7. 5% WER, meanwhile reduces 90th percentile latency from 210 ms to only 30 ms on LibriSpeech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Fast and Accurate Streaming End-to-End ASR
RNN-T EP+LAS, together with MWER training brings in 18. 7% relative WER reduction and 160ms 90-percentile latency reductions compared to the original proposed RNN-T EP model.
Audio and Speech Processing
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.
On Neural Phone Recognition of Mixed-Source ECoG Signals
The emerging field of neural speech recognition (NSR) using electrocorticography has recently attracted remarkable research interest for studying how human brains recognize speech in quiet and noisy surroundings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Personal VAD: Speaker-Conditioned Voice Activity Detection
In this paper, we propose "personal VAD", a system to detect the voice activity of a target speaker at the frame level.
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing.
Streaming End-to-end Speech Recognition For Mobile Devices
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition.
