Search Results for author:
Found 33 papers, 8 papers with code
Training and Inference Efficiency of Encoder-Decoder Speech Models
However, the reported data and compute requirements for their training are prohibitive for many in the research community.
EMMeTT: Efficient Multimodal Machine Translation Training
This work focuses on neural machine translation (NMT) and proposes a joint multimodal training regime of Speech-LLM to include automatic speech translation (AST).
Chain-of-Thought Prompting for Speech Translation
Building on the success of text-based LLMs, recent research has adapted these models to use speech embeddings for prompting, resulting in Speech-LLM models that exhibit strong performance in automatic speech recognition (ASR) and automatic speech translation (AST).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition
Given recent advances in generative AI technology, a key question is how large language models (LLMs) can enhance acoustic modeling tasks using text decoding results from a frozen, pretrained automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
BESTOW: Efficient and Streamable Speech Language Model with the Best of Two Worlds in GPT and T5
We propose BESTOW architecture to bring the BESt features from TwO Worlds into a single model that is highly efficient and has strong multitask capabilities.
Less is More: Accurate Speech Recognition & Translation without Web-Scale Data
Recent advances in speech recognition and translation rely on hundreds of thousands of hours of Internet speech data.
Regularizing Contrastive Predictive Coding for Speech Applications
These representations significantly reduce the amount of labeled data needed for downstream task performance, such as automatic speech recognition.
Delay-penalized transducer for low-latency streaming ASR
In streaming automatic speech recognition (ASR), it is desirable to reduce latency as much as possible while having minimum impact on recognition accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Fast and parallel decoding for transducer
In this work, we introduce a constrained version of transducer loss to learn strictly monotonic alignments between the sequences; we also improve the standard greedy search and beam search algorithms by limiting the number of symbols that can be emitted per time step in transducer decoding, making it more efficient to decode in parallel with batches.
Time-domain speech super-resolution with GAN based modeling for telephony speaker verification
We show that our bandwidth extension leads to phenomena such as a shift of telephone (test) embeddings towards wideband (train) signals, a negative correlation of perceptual quality with downstream performance, and condition-independent score calibration.
Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations
This paper applies a non-contrastive self-supervised learning method on an unlabeled speech corpus to learn utterance-level embeddings.
Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition
In this paper, we 1) investigate the influence of different factors (i. e., model architecture, phonotactic model, type of speech representation) on phone recognition in an unknown language; 2) provide an analysis of which phones transfer well across languages and which do not in order to understand the limitations of and areas for further improvement for automatic phone inventory creation; and 3) present different methods to build a phone inventory of an unseen language in an unsupervised way.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Unsupervised Speech Segmentation and Variable Rate Representation Learning using Segmental Contrastive Predictive Coding
We overcome this limitation with a segmental contrastive predictive coding (SCPC) framework to model the signal structure at a higher level, e. g., phone level.
Joint prediction of truecasing and punctuation for conversational speech in low-resource scenarios
Further, we show that by training the model in the written text domain and then transfer learning to conversations, we can achieve reasonable performance with less data.
Beyond Isolated Utterances: Conversational Emotion Recognition
While most of the current approaches focus on inferring emotion from isolated utterances, we argue that this is not sufficient to achieve conversational emotion recognition (CER) which deals with recognizing emotions in conversations.
Representation Learning to Classify and Detect Adversarial Attacks against Speaker and Speech Recognition Systems
Also, representations trained to classify attacks against speaker identification can be used also to classify attacks against speaker verification and speech recognition.
What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition
Dialog acts can be interpreted as the atomic units of a conversation, more fine-grained than utterances, characterized by a specific communicative function.
Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation
We overcome this limitation with a segmental contrastive predictive coding (SCPC) framework that can model the signal structure at a higher level e. g. at the phoneme level.
Deep Feature CycleGANs: Speaker Identity Preserving Non-parallel Microphone-Telephone Domain Adaptation for Speaker Verification
We investigate it for adapt microphone speech to the telephone domain.
Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation
In the first stage, a recently proposed method in the task of unsupervised subword modeling is improved by replacing a monolingual out-of-domain (OOD) ASR system with a multilingual one to create a subword-discriminative representation that is more language-independent.
Adversarial Attacks and Defenses for Speech Recognition Systems
We investigate two threat models: a denial-of-service scenario where fast gradient-sign method (FGSM) or weak projected gradient descent (PGD) attacks are used to degrade the model's word error rate (WER); and a targeted scenario where a more potent imperceptible attack forces the system to recognize a specific phrase.
Study of Pre-processing Defenses against Adversarial Attacks on State-of-the-art Speaker Recognition Systems
Such attacks pose severe security risks, making it vital to deep-dive and understand how much the state-of-the-art SR systems are vulnerable to these attacks.
Focus on the present: a regularization method for the ASR source-target attention layer
This paper introduces a novel method to diagnose the source-target attention in state-of-the-art end-to-end speech recognition models with joint connectionist temporal classification (CTC) and attention training.
CopyPaste: An Augmentation Method for Speech Emotion Recognition
Data augmentation is a widely used strategy for training robust machine learning models.
How Phonotactics Affect Multilingual and Zero-shot ASR Performance
Furthermore, we find that a multilingual LM hurts a multilingual ASR system's performance, and retaining only the target language's phonotactic data in LM training is preferable.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
WER we are and WER we think we are
Natural language processing of conversational speech requires the availability of high-quality transcripts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Self-Expressing Autoencoders for Unsupervised Spoken Term Discovery
We perform segmentation based on the assumption that the frame feature vectors are more similar within a segment than across the segments.
That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages
Only a handful of the world's languages are abundant with the resources that enable practical applications of speech processing technologies.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Hierarchical Transformers for Long Document Classification
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a recently introduced language representation model based upon the transfer learning paradigm.
Avaya Conversational Intelligence: A Real-Time System for Spoken Language Understanding in Human-Human Call Center Conversations
Avaya Conversational Intelligence(ACI) is an end-to-end, cloud-based solution for real-time Spoken Language Understanding for call centers.
Towards Better Understanding of Spontaneous Conversations: Overcoming Automatic Speech Recognition Errors With Intent Recognition
In this paper, we present a method for correcting automatic speech recognition (ASR) errors using a finite state transducer (FST) intent recognition framework.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Punctuation Prediction Model for Conversational Speech
The models are trained on the Fisher corpus which includes punctuation annotation.
Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient?
In this paper, the problem of recovery of morphological information lost in abbreviated forms is addressed with a focus on highly inflected languages.
