Search Results for author:
Found 20 papers, 3 papers with code
Augmenting text for spoken language understanding with Large Language Models
Using the generated text with JAT and TTS for spoken semantic parsing improves EM on STOP by 1. 4% and 2. 6% absolute for existing and new domains respectively.
Modality Confidence Aware Training for Robust End-to-End Spoken Language Understanding
End-to-end (E2E) spoken language understanding (SLU) systems that generate a semantic parse from speech have become more promising recently.
Introducing Semantics into Speech Encoders
Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+10
Joint Audio/Text Training for Transformer Rescorer of Streaming Speech Recognition
In this work, we present our Joint Audio/Text training method for Transformer Rescorer, to leverage unpaired text-only data which is relatively cheaper than paired audio-text data.
Deliberation Model for On-Device Spoken Language Understanding
We propose a novel deliberation-based approach to end-to-end (E2E) spoken language understanding (SLU), where a streaming automatic speech recognition (ASR) model produces the first-pass hypothesis and a second-pass natural language understanding (NLU) component generates the semantic parse by conditioning on both ASR's text and audio embeddings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric
Measuring automatic speech recognition (ASR) system quality is critical for creating user-satisfying voice-driven applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding
We define SemDist as the distance between a reference and hypothesis pair in a sentence-level embedding space.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+14
Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion
How to leverage dynamic contextual information in end-to-end speech recognition has remained an active research area.
Improving RNN Transducer Based ASR with Auxiliary Tasks
End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results compared to conventional hybrid speech recognizers.
Ranked #15 on
Speech Recognition
on LibriSpeech test-clean
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducer
Recurrent Neural Network Transducer (RNN-T), like most end-to-end speech recognition model architectures, has an implicit neural network language model (NNLM) and cannot easily leverage unpaired text data during training.
Cross-Attention End-to-End ASR for Two-Party Conversations
We present an end-to-end speech recognition model that learns interaction between two speakers based on the turn-changing information.
Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion
We present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings.
Acoustic-to-Word Models with Conversational Context Information
Conversational context information, higher-level knowledge that spans across sentences, can help to recognize a long conversation.
Dialog-context aware end-to-end speech recognition
Existing speech recognition systems are typically built at the sentence level, although it is known that dialog context, e. g. higher-level knowledge that spans across sentences or speakers, can help the processing of long conversations.
Towards Language-Universal End-to-End Speech Recognition
Building speech recognizers in multiple languages typically involves replicating a monolingual training recipe for each language, or utilizing a multi-task learning approach where models for different languages have separate output labels but share some internal parameters.
Improved training for online end-to-end speech recognition systems
Achieving high accuracy with end-to-end speech recognizers requires careful parameter initialization prior to training.
Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning
Recently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments.
Environmental Noise Embeddings for Robust Speech Recognition
We propose a novel deep neural network architecture for speech recognition that explicitly employs knowledge of the background environmental noise within a deep neural network acoustic model.
Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition
Integration of multiple microphone data is one of the key ways to achieve robust speech recognition in noisy environments or when the speaker is located at some distance from the input device.
Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition
We propose a transfer deep learning (TDL) framework that can transfer the knowledge obtained from a single-modal neural network to a network with a different modality.
