Search Results for author:
Found 21 papers, 1 papers with code
On Evaluating and Comparing Open Domain Dialog Systems
In this paper, we propose a comprehensive evaluation strategy with multiple metrics designed to reduce subjectivity by selecting metrics which correlate well with human judgement.
Topic-based Evaluation for Conversational Bots
Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined.
Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting
Finally, the max-pooling loss trained LSTM initialized with a cross-entropy pre-trained network shows the best performance, which yields $67. 6\%$ relative reduction compared to baseline feed-forward DNN in Area Under the Curve (AUC) measure.
Contextual Language Model Adaptation for Conversational Agents
Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Data Augmentation for Robust Keyword Spotting under Playback Interference
Accurate on-device keyword spotting (KWS) with low false accept and false reject rate is crucial to customer experience for far-field voice control of conversational agents.
Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning
For real-world speech recognition applications, noise robustness is still a challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Scalable Multi Corpora Neural Language Models for ASR
Neural language models (NLM) have been shown to outperform conventional n-gram language models by a substantial margin in Automatic Speech Recognition (ASR) and other tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces
Finally, we contrast these methods to a jointly trained end-to-end joint SLU model, consisting of ASR and NLU subsystems which are connected by a neural network based interface instead of text, that produces transcripts as well as NLU interpretation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multi-task Language Modeling for Improving Speech Recognition of Rare Words
We show that our rescoring model trained with these additional tasks outperforms the baseline rescoring model, trained with only the language modeling task, by 1. 4% on a general test and by 2. 6% on a rare word test set in terms of word-error-rate relative (WERR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding
Spoken language understanding (SLU) systems extract transcriptions, as well as semantics of intent or named entities from speech, and are essential components of voice activated systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End
On the other hand, a streaming system using per-frame intent posteriors as extra inputs for the RNN-T ASR system yields a 3. 33% relative WERR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
On joint training with interfaces for spoken language understanding
Spoken language understanding (SLU) systems extract both text transcripts and semantics associated with intents and slots from input speech utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding
Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio.
Natural Language Understanding
Spoken Language Understanding
MTL-SLT: Multi-Task Learning for Spoken Language Tasks
The proposed framework benefits from three key aspects: 1) pre-trained sub-networks of ASR model and language model; 2) multi-task learning objective to exploit shared knowledge from different tasks; 3) end-to-end training of ASR and downstream NLP task based on sequence loss.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale
For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparities
As for other forms of AI, speech recognition has recently been examined with respect to performance disparities across different user cohorts.
Adaptive Endpointing with Deep Contextual Multi-armed Bandits
In this paper, we aim to provide a solution for adaptive endpointing by proposing an efficient method for choosing an optimal endpointing configuration given utterance-level audio features in an online setting, while avoiding hyperparameter grid-search.
Cross-utterance ASR Rescoring with Graph-based Label Propagation
We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity.
Federated Self-Learning with Weak Supervision for Speech Recognition
Automatic speech recognition (ASR) models with low-footprint are increasingly being deployed on edge devices for conversational agents, which enhances privacy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Two-pass Endpoint Detection for Speech Recognition
Endpoint (EP) detection is a key component of far-field speech recognition systems that assist the user through voice commands.
Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion
We propose an approach for continuous prediction of turn-taking and backchanneling locations in spoken dialogue by fusing a neural acoustic model with a large language model (LLM).
