12 accepted papers
Audio is a critical component of multimodal perception, and any truly intelligent system must demonstrate a wide range of auditory capabilities. These capabilities include transcription, classification, retrieval, reasoning, segmentation, clustering, reranking, and reconstruction. Fundamentally, eac…
Existing training criteria in automatic speech recognition (ASR) permit the model to freely explore more than one time alignments between the feature and label sequences. In this paper, we use entropy to measure a model’s uncertainty, i.e. how it chooses to distribute the probability mass over the s…
We propose JEIT, a joint end-to-end (E2E) model and internal language model (ILM) training method to inject large-scale unpaired text into ILM during E2E training which improves rare-word speech recognition. With JEIT, the E2E model computes an E2E loss on audio-transcript pairs while its ILM estima…
We introduce LAST, a LAttice-based Speech Transducer library in JAX. With an emphasis on flexibility, ease-of-use, and scalability, LAST implements differentiable weighted finite state automaton (WFSA) algorithms needed for training & inference that scale to a large WFSA such as a recognition lattic…
We introduce the Globally Normalized Autoregressive Transducer (GNAT) for addressing the label bias problem in streaming speech recognition. Our solution admits a tractable exact computation of the denominator for the sequence-level normalization. Through theoretical and empirical results, we demons…
Second-pass rescoring is a well known technique to improve the performance of Automatic Speech Recognition (ASR) systems. Neural Oracle Search (NOS), which selects the most likely hypothesis from an N-best hypothesis list by integrating information from multiple sources, such as the input acoustic r…
End-to-end (E2E) automatic speech recognition (ASR) models, by now, have shown competitive performance on several benchmarks. These models are structured to either operate in streaming or non-streaming mode. This work presents cascaded encoders for building a single E2E ASR model that can operate in…
This paper proposes and evaluates the hybrid autoregressive transducer (HAT) model, a time-synchronous encoder-decoder model that preserves the modularity of conventional automatic speech recognition systems. The HAT model provides a way to measure the quality of the internal language model that can…
In this paper, we propose a neural search algorithm to select the most likely hypothesis using a sequence of acoustic representations and multiple hypotheses as input. The algorithm provides a sequence level score for each audio-hypothesis pair that is obtained by integrating information from multip…
Most of the parameters in large vocabulary models are used in embedding layer to map categorical features to vectors and in softmax layer for classification weights. This is a bottleneck in memory constraint on-device training applications like federated learning and on-device inference applications…
This article introduces and evaluates Sampled Connectionist Temporal Classification (CTC) which connects the CTC criterion to the Cross Entropy (CE) objective through sampling. Instead of computing the logarithm of the sum of the alignment path likelihoods, at each training step the sampled CTC only…
This article proposes and evaluates a Gaussian Mixture Model (GMM) represented as the last layer of a Deep Neural Network (DNN) architecture and jointly optimized with all previous layers using Asynchronous Stochastic Gradient Descent (ASGD). The resulting “Deep GMM” architecture was investigated wi…