22 accepted papers
In this paper, we explore an alternate approach to the popular method of using large language models (LLMs) as a second decoder for Automated Speech Recognition (ASR) and speech understanding tasks. We propose to employ diffusion networks to generate a correction signal that can be applied on the or…
Multilingual speech recognition systems often use an input language code in order to prompt the transcription in the target language. However, the spoken language in the input audio may not always match the language code, as often prevalent in multilingual societies. This language mismatch can signi…
Recently, large-scale pre-trained speech encoders and Large Language Models (LLMs) have been released, which show state-of-the-art performance on a range of spoken language processing tasks, including Automatic Speech Recognition (ASR). To effectively combine both models for better performance, cont
To surpass human-level accuracy, speech recognition models must go beyond relying solely on human labels. To this end, we must build stronger models from weaker supervisors and this is the main goal in weak-to-strong generalization (WSG). WSG methods normally incorporate additional information into…
Vector representations of text and speech signals such as word2vec and wav2vec are used commonly in automatic speech recognition (ASR) and spoken language understanding systems. Recent results in natural language processing have proposed a task vector, defined as the difference vector between a mode…
In this work, we study the impact of Large-scale Language Models (LLM) on Automated Speech Recognition (ASR) of YouTube videos, which we use as a source for long-form ASR. We demonstrate up to 8% relative reduction in Word Error Eate (WER) on US English (en-us) and code-switched Indian English (en-i…
The state-of-the-art conformer used in automatic speech recognition combines feed-forward, convolution and multi-headed self-attention layers in a single model that is trained end-to-end with a decoder network. While this end-to-end training is simple and beneficial for word error rate, it restricts…
This work studies knowledge distillation (KD) and addresses its constraints for recurrent neural network transducer (RNN-T) models. In hard distillation, a teacher model transcribes large amounts of unlabelled speech to train a student model. Soft distillation is another popular KD method that disti…
Regularization and data augmentation are crucial to training end-to-end automatic speech recognition systems. Dropout is a popular regularization technique, which operates on each neuron independently by multiplying it with a Bernoulli random variable. We propose a generalization of dropout, called…
We present a beam decoding strategy for recurrent neural network transducers which has the characteristic that all competing hypotheses within the beam have the same alignment length (number of output symbols plus BLANK symbols). We contrast the proposed technique with time-synchronous decoding wher…
Training an end-to-end (E2E) neural network speech-to-intent (S2I) system that directly extracts intents from speech requires large amounts of intent-labeled speech data, which is time consuming and expensive to collect. Initializing the S2I model with an ASR model trained on copious speech data can…
Direct acoustics-to-word (A2W) systems for end-to-end automatic speech recognition are simpler to train, and more efficient to decode with, than sub-word systems. However, A2W systems can have difficulties at training time when data is limited, and at decoding time when recognizing words outside the…
We present a simple noise injection algorithm for training end-to-end ASR models which consists in adding to the spectra of training utterances the scaled spectra of random utterances of comparable length. We conjecture that the sequence information of the "noise" utterances is important and verify…
Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received increasing attention compared to conventional subword based automatic speech recognition models using phones, characters, or context-dependent hidden Markov model states. This is because A2W models recognize words from sp…
The performance of automatic speech recognition systems degrades with increasing mismatch between the training and testing scenarios. Differences in speaker accents are a significant source of such mismatch. The traditional approach to deal with multiple accents involves pooling data from several ac…
Recurrent neural networks have become increasingly popular for the task of language modeling achieving impressive gains in state-of-the-art speech recognition and natural language processing (NLP) tasks. Recurrent models exploit word dependencies over a much longer context window (as retained by the…
End-to-end (E2E) systems have achieved competitive results compared to conventional hybrid hidden Markov model (HMM)-deep neural network based automatic speech recognition (ASR) systems. Such E2E systems are attractive due to the lack of dependence on alignments between input acoustic and output gra…
In recent years, so-called, “end-to-end” speech recognition systems have emerged as viable alternatives to traditional ASR frameworks. Keyword search, localizing an orthographic query in a speech corpus, is typically performed by using automatic speech recognition (ASR) to generate an index. Previou…
This paper investigates the effectiveness of knowledge distillation in the context of multilingual models. We show that with knowledge distillation, Long Short-Term Memory(LSTM) models can be used to train standard feed-forward Deep Neural Network (DNN) models for a variety of low-resource languages…
Recent evidences suggest that the performance of kernel methods may match that of deep neural networks (DNNs), which have been the state-of-the-art approach for speech recognition. In this work, we present an improvement of the kernel ridge regression studied in Huang et al., ICASSP 2014, and show t…
Semantic word embeddings have become increasingly important in natural language processing tasks over the last few years. This popularity is due to their ability to easily capture rich semantic information through a distributed representation and the availability of fast and scalable algorithms for…
Pathological speech involves atypical speech production which may result from several factors including oral diseases, physical disabilities in the voice production system and atypical anatomy. Automatic evaluation of intelligibility in patients with pathological speech can assist accurate diagnosis…