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Found 27 papers, 4 papers with code
Self-Supervised Speech Representation Learning: A Review
Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources, both of which have seen active research for many years.
Domain Prompts: Towards memory and compute efficient domain adaptation of ASR systems
Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains creating a need to adapt to new domains with small memory and deployment overhead.
Directed Speech Separation for Automatic Speech Recognition of Long Form Conversational Speech
Also, most of these models are trained with synthetic mixtures and do not generalize to real conversational data.
Representation learning through cross-modal conditional teacher-student training for speech emotion recognition
To improve the efficacy of our approach, we propose a novel estimate of the quality of the emotion predictions, to condition teacher-student training.
Prompt-tuning in ASR systems for efficient domain-adaptation
In this work, we overcome the problem using prompt-tuning, a methodology that trains a small number of domain token embedding parameters to prime a transformer-based LM to a particular domain.
Remember the context! ASR slot error correction through memorization
Accurate recognition of slot values such as domain specific words or named entities by automatic speech recognition (ASR) systems forms the core of the Goal-oriented Dialogue Systems.
ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling
In this paper, we investigate various techniques to improve contextualization, content word robustness and domain adaptation of a Transformer-XL neural language model (NLM) to rescore ASR N-best hypotheses.
Speaker-conversation factorial designs for diarization error analysis
Determining the cause of diarization errors is difficult because speaker voice acoustics and conversation structure co-vary, and the interactions between acoustics, conversational structure, and diarization accuracy are complex.
Adapting Long Context NLM for ASR Rescoring in Conversational Agents
Neural Language Models (NLM), when trained and evaluated with context spanning multiple utterances, have been shown to consistently outperform both conventional n-gram language models and NLMs that use limited context.
Contextual Biasing of Language Models for Speech Recognition in Goal-Oriented Conversational Agents
In this paper, we explore different ways to incorporate context into a LSTM based NLM in order to model long range dependencies and improve speech recognition.
Neural Inverse Text Normalization
We propose an efficient and robust neural solution for ITN leveraging transformer based seq2seq models and FST-based text normalization techniques for data preparation.
Transformer-Transducers for Code-Switched Speech Recognition
We live in a world where 60% of the population can speak two or more languages fluently.
Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment
Non-autoregressive models greatly improve decoding speed over typical sequence-to-sequence models, but suffer from degraded performance.
Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech
Experiments conducted on the Fisher corpus show that our proposed approach achieves ~6-9% and ~3-4% absolute improvement (F1 score) over the baseline BLSTM model on reference transcripts and ASR outputs respectively.
Robust Prediction of Punctuation and Truecasing for Medical ASR
We also present techniques for domain and task specific adaptation by fine-tuning masked language models with medical domain data.
Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition
We propose a novel approach to semi-supervised automatic speech recognition (ASR).
Masked Language Model Scoring
Instead, we evaluate MLMs out of the box via their pseudo-log-likelihood scores (PLLs), which are computed by masking tokens one by one.
BERTphone: Phonetically-Aware Encoder Representations for Utterance-Level Speaker and Language Recognition
We introduce BERTphone, a Transformer encoder trained on large speech corpora that outputs phonetically-aware contextual representation vectors that can be used for both speaker and language recognition.
Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems
With the advent of conversational assistants, like Amazon Alexa, Google Now, etc., dialogue systems are gaining a lot of traction, especially in industrial setting.
Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition
The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition.
Context Models for OOV Word Translation in Low-Resource Languages
Out-of-vocabulary word translation is a major problem for the translation of low-resource languages that suffer from a lack of parallel training data.
Syntactic and Semantic Features For Code-Switching Factored Language Models
The experimental results reveal that Brown word clusters, part-of-speech tags and open-class words are the most effective at reducing the perplexity of factored language models on the Mandarin-English Code-Switching corpus SEAME.
Exploiting Out-of-Domain Data Sources for Dialectal Arabic Statistical Machine Translation
Statistical machine translation for dialectal Arabic is characterized by a lack of data since data acquisition involves the transcription and translation of spoken language.
