Search Results for author:
Found 25 papers, 1 papers with code
Toward domain-invariant speech recognition via large scale training
More importantly, such models generalize better to unseen conditions and allow for rapid adaptation -- we show that by using as little as 10 hours of data from a new domain, an adapted domain-invariant model can match performance of a domain-specific model trained from scratch using 70 times as much data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
From Audio to Semantics: Approaches to end-to-end spoken language understanding
Conventional spoken language understanding systems consist of two main components: an automatic speech recognition module that converts audio to a transcript, and a natural language understanding module that transforms the resulting text (or top N hypotheses) into a set of domains, intents, and arguments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Recognizing long-form speech using streaming end-to-end models
In this work, we examine the ability of E2E models to generalize to unseen domains, where we find that models trained on short utterances fail to generalize to long-form speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A comparison of end-to-end models for long-form speech recognition
In this paper, we both investigate and improve the performance of end-to-end models on long-form transcription.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions
On a long-form YouTube test set, when the nonstreaming RNN-T model is trained with shorter segments of data, the proposed combination improves word error rate (WER) from 22. 3% to 14. 8%; when the streaming RNN-T model trained on short Search queries, the proposed techniques improve WER on the YouTube set from 67. 0% to 25. 3%.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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
Improving Streaming Automatic Speech Recognition With Non-Streaming Model Distillation On Unsupervised Data
We propose a novel and effective learning method by leveraging a non-streaming ASR model as a teacher to generate transcripts on an arbitrarily large data set, which is then used to distill knowledge into streaming ASR models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Cascaded encoders for unifying streaming and non-streaming ASR
The proposed model consists of streaming and non-streaming encoders.
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
Less Is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging
End-to-end models that condition the output label sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Virtual Microgrid Management via Software-defined Energy Network for Electricity Sharing
Digitalization has led to radical changes in the distribution of goods across various sectors.
Personalized Keyphrase Detection using Speaker and Environment Information
In this paper, we introduce a streaming keyphrase detection system that can be easily customized to accurately detect any phrase composed of words from a large vocabulary.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Packetized Energy Management Controller for Residential Consumers
In this paper, we investigate the management of energy storage control and load scheduling in scenarios considering a grid-connected photovoltaic (PV) system using packetized energy management.
Cross-attention conformer for context modeling in speech enhancement for ASR
This work introduces \emph{cross-attention conformer}, an attention-based architecture for context modeling in speech enhancement.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
SNRi Target Training for Joint Speech Enhancement and Recognition
Furthermore, by analyzing the predicted target SNRi, we observed the jointly trained network automatically controls the target SNRi according to noise characteristics.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Conformer-based ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement and Speech Separation
Compared to the noisy baseline, the joint model reduces the word error rate in low signal-to-noise ratio conditions by at least 71% on our echo cancellation dataset, 10% on our noisy dataset, and 26% on our multi-speaker dataset.
Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition
Personalization of on-device speech recognition (ASR) has seen explosive growth in recent years, largely due to the increasing popularity of personal assistant features on mobile devices and smart home speakers.
Extracting Targeted Training Data from ASR Models, and How to Mitigate It
We design Noise Masking, a fill-in-the-blank style method for extracting targeted parts of training data from trained ASR models.
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers
This work introduces the Cleanformer, a streaming multichannel neural based enhancement frontend for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Mask scalar prediction for improving robust automatic speech recognition
Using neural network based acoustic frontends for improving robustness of streaming automatic speech recognition (ASR) systems is challenging because of the causality constraints and the resulting distortion that the frontend processing introduces in speech.
A Conformer-based Waveform-domain Neural Acoustic Echo Canceller Optimized for ASR Accuracy
Acoustic Echo Cancellation (AEC) is essential for accurate recognition of queries spoken to a smart speaker that is playing out audio.
Streaming Noise Context Aware Enhancement For Automatic Speech Recognition in Multi-Talker Environments
First is the Context Aware Beamformer which uses the noise context and detected hotword to determine how to target the desired speaker.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Universally-Deployable ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement, and Voice Separation
The joint model uses contextual information, such as a reference of the playback audio, noise context, and speaker embedding.
Extreme Encoder Output Frame Rate Reduction: Improving Computational Latencies of Large End-to-End Models
In the present work, we study one such strategy: applying multiple frame reduction layers in the encoder to compress encoder outputs into a small number of output frames.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
