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Ehsan Variani

Ehsan Variani

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    Preview abstract 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 N-best hypothesis list by integrating in-formation from multiple sources, such as the input acoustic representations, N-best hypotheses, additional first-pass statistics,and unpaired textual information through an external language model, has shown success in re-scoring for RNN-T first-pass models. Multilingual first-pass speech recognition models of-ten outperform their monolingual counterparts when trained on related or low-resource languages. In this paper, we investigate making the second-pass model multilingual and apply rescoring on a multilingual first-pass. We conduct experiments on Nordic languages including Danish, Dutch, Finnish, Norwegian and Swedish. View details
    Preview abstract Personalization of speech models on mobile devices (on-device personalization) is an active area of research, but more often than not, mobile devices have more text-only data than paired audio-text data. We explore training a personalized language model on text-only data, used during inference to improve speech recognition performance for that user. We experiment on a user-clustered LibriSpeech corpus, supplemented with personalized text-only data for each user from Project Gutenberg. We release this User-Specific LibriSpeech (UserLibri) dataset to aid future personalization research. LibriSpeech audio-transcript pairs are grouped into 55 users from the test-clean dataset and 52 users from test-other. We are able to lower the average word error rate per user across both sets in streaming and nonstreaming models, including an improvement of 2.5 for the harder set of test-other users when streaming. View details
    Preview abstract This paper explores ways to improve a two-pass speech recognition system when the first-pass is hybrid autoregressive transducer model and the second-pass is a neural language model. The main focus is on the scores provided by each of these models, their quantitative analysis, how to improve them and the best way to integrate them with the objective of better recognition accuracy. Several analysis are presented to show the importance of the choice of the integration weights for combining the first-pass and the second-pass scores. A sequence level weight estimation model along with four training criteria are proposed which allow adaptive integration of the scores per acoustic sequence. The effectiveness of this algorithm is demonstrated by constructing and analyzing models on the Librispeech data set. View details
    Preview abstract On-device end-to-end (E2E) models have shown improvementsover a conventional model on Search test sets in both quality, as measured by Word Error Rate (WER), and latency, measured by the time the result is finalized after the user stops speaking. However, the E2E model is trained on a small fraction of audio-text pairs compared to the 100 billion text utterances that a conventional language model (LM) is trained with. Thus E2E models perform poorly on rare words and phrases. In this paper, building upon the two-pass streaming Cascaded Encoder E2E model, we explore using a Hybrid Autoregressive Transducer (HAT) factorization to better integrate an on-device neural LM trained on text-only data. Furthermore, to further improve decoder latency we introduce a non-recurrent embedding decoder, in place of the typical LSTM decoder, into the Cascaded Encoder model. Overall, we present a streaming on-device model that incorporates an external neural LM and outperforms the conventional model in both search and rare-word quality, as well as latency, and is 318X smaller. View details
    Hybrid Autoregressive Transducer (HAT)
    ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain, pp. 6139-6143
    Preview abstract 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 be used to decide whether inference with an external language model is beneficial or not. We evaluate our proposed model on a large-scale voice search task. Our experiments show significant improvements in WER compared to the state-of-the-art approaches. View details
    Preview abstract Modeling tasks that use a large vocabulary require two words-to-vector maps, one for the embedding layer and one for the softmax layer. A majority of model parameters for such modeling tasks are in the embedding and the softmax layers, while only a small fraction of the parameters are used to the core of the model e.g., recurrent structures such as LSTM. When training models on small to medium corpus size, these models are subject to over-tting as well as large storage and memory footprint requirements. We propose to compress the embedding and softmax matrices by imposing structure into the parameter space. The embedding and softmax matrices are factored as the product of a sparse matrix and a structured dense matrix. Without compromizing performance, we achieve a significant compression rate for the embedding layer and a moderate compression rate for the softmax layer. The factoring of the embedding and softmax matrix before training allows us to jointly train these matrix values to optimize the training objective. Being able to compress the embedding and softmax layers allows us to uses this saved memory for increased recurrent unit size, which results in improved performance at an uncompressed memory footprint. We report results of this compression technique on standard datasets and a state of the art on-device automatic speech recognition system. View details
    Preview abstract 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 com- puting the logarithm of the sum of the alignment path likelihoods, at each training step the sampled CTC only computes the CE loss be- tween the sampled alignment path and model posteriors. It is shown that the sampled CTC objective is an unbiased estimator of an upper bound for the CTC loss, thus minimization of the sampled CTC is equivalent to the minimization of the upper bound of the CTC ob- jective. The definition of the sampled CTC objective has the advan- tage that it is scalable computationally to the massive datasets using accelerated computation machines. The sampled CTC is compared with CTC in two large-scale speech recognition tasks and it is shown that sampled CTC can achieve similar WER performance of the best CTC baseline in about one fourth of the training time of the CTC baseline. View details
    Preview abstract This article discusses strategies for end-to-end training of state- of-the-art acoustic models for Large Vocabulary Continuous Speech Recognition (LVCSR), with the goal of leveraging Ten- sorFlow components so as to make efficient use of large-scale training sets, large model sizes, and high-speed computation units such as Graphical Processing Units (GPUs). Benchmarks are presented that evaluate the efficiency of different approaches to batching of training data, unrolling of recurrent acoustic models, and device placement of TensorFlow variables and op- erations. An overall training architecture developed in light of those findings is then described. The approach makes it possi- ble to take advantage of both data parallelism and high speed computation on GPU for state-of-the-art sequence training of acoustic models. The effectiveness of the design is evaluated for different training schemes and model sizes, on a 20, 000 hour Voice Search task. View details
    Preview abstract Multichannel ASR systems commonly separate speech enhancement, including localization, beamforming and postfiltering, from acoustic modeling. In this chapter, we perform multi-channel enhancement jointly with acoustic modeling in a deep neural network framework. Inspired by beamforming, which leverages differences in the fine time structure of the signal at different microphones to filter energy arriving from different directions, we explore modeling the raw time-domain waveform directly. We introduce a neural network architecture which performs multichannel filtering in the first layer of the network and show that this network learns to be robust to varying target speaker direction of arrival, performing as well as a model that is given oracle knowledge of the true target speaker direction. Next, we show how performance can be improved by factoring the first layer to separate the multichannel spatial filtering operation from a single channel filterbank which computes a frequency decomposition. We also introduce an adaptive variant, which updates the spatial filter coefficients at each time frame based on the previous inputs. Finally we demonstrate that these approaches can be implemented more efficiently in the frequency domain. Overall, we find that such multichannel neural networks give a relative word error rate improvement of more than 5% compared to a traditional beamforming-based multichannel ASR system and more than 10% compared to a single channel waveform model. View details
    Preview abstract In this paper, we describe how to efficiently implement an acoustic room simulator to generate large-scale simulated data for training deep neural networks. Even though Google Room Simulator in [1] was shown to be quite effective in reducing the Word Error Rates (WERs) for far-field applications by generating simulated far-field training sets, it requires a very large number of Fast Fourier Transforms (FFTs) of large size. Room Simulator in [1] used approximately 80 percent of Central Processing Unit (CPU) usage in our CPU + Graphics Processing Unit (GPU) training architecture [2]. In this work, we implement an efficient OverLap Addition (OLA) based filtering using the open-source FFTW3 library. Further, we investigate the effects of the Room Impulse Response (RIR) lengths. Experimentally, we conclude that we can cut the tail portions of RIRs whose power is less than 20 dB below the maximum power without sacrificing the speech recognition accuracy. However, we observe that cutting RIR tail more than this threshold harms the speech recognition accuracy for rerecorded test sets. Using these approaches, we were able to reduce CPU usage for the room simulator portion down to 9.69 percent in CPU/GPU training architecture. Profiling result shows that we obtain 22.4 times speed-up on a single machine and 37.3 times speed up on Google's distributed training infrastructure. View details
    Preview abstract This paper describes the technical and system building advances made to the Google Home multichannel speech recognition system, which was launched in November 2016. Technical advances include an adaptive dereverberation frontend, the use of neural network models that do multichannel processing jointly with acoustic modeling, and grid lstms to model frequency variations. On the system level, improvements include adapting the model using Google Home specific data. We present results on a variety of multichannel sets. The combination of technical and system advances result in a reduction of WER of over 18\% relative compared to the current production system. View details
    Preview abstract Multichannel ASR systems commonly separate speech enhancement, including localization, beamforming and postfiltering, from acoustic modeling. In this paper, we perform multichannel enhancement jointly with acoustic modeling in a deep neural network framework. Inspired by beamforming, which leverages differences in the fine time structure of the signal at different microphones to filter energy arriving from different directions, we explore modeling the raw time-domain waveform directly. We introduce a neural network architecture which performs multichannel filtering in the first layer of the network and show that this network learns to be robust to varying target speaker direction of arrival, performing as well as a model that is given oracle knowledge of the true target speaker direction. % Next, we show how performance can be improved by \emph{factoring} the first layer to separate the multichannel spatial filtering operation from a single channel filterbank which computes a frequency decomposition. % We also introduce an adaptive variant, which updates the spatial filter coefficients at each time frame based on the previous inputs. % Finally we demonstrate that these approaches can be implemented more efficiently in the frequency domain. Overall, we find that such multichannel neural networks give a relative word error rate improvement of more than 5\% compared to a traditional beamforming-based multichannel ASR system and more than 10\% compared to a single channel waveform model. View details
    Preview abstract State-of-the-art automatic speech recognition (ASR) systems typically rely on pre-processed features. This paper studies the time-frequency duality in ASR feature extraction methods and proposes extending the standard acoustic model with a complex-valued linear projection layer to learn and optimize features that minimize standard cost functions such as cross entropy. The proposed Complex Linear Projection (CLP) features achieve superior performance compared to pre-processed Log Mel features. View details
    Preview abstract 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 with special attention to the following issues: (1) The extent to which joint optimization improves over separate optimization of the DNN-based feature extraction layers and the GMM layer; (2) The extent to which depth (measured in number of layers, for a matched total number of parameters) helps a deep generative model based on the GMM layer, compared to a vanilla DNN model; (3) Head-to-head performance of Deep GMM architectures vs. equivalent DNN architectures of comparable depth, using the same optimization criterion (frame-level Cross Entropy (CE)) and optimization method (ASGD); (4) Expanded possibilities for modeling offered by the Deep GMM generative model. The proposed Deep GMMs were found to yield Word Error Rates (WERs) competitive with state-of-the-art DNN systems, at the cost of pre-training using standard DNNs to initialize the Deep GMM feature extraction layers. An extension to Deep Subspace GMMs is described, resulting in additional gains. View details
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