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Speech recognition

Turning speech into text is at the heart of an amazing variety of products and services that enrich peoples’ lives. Most of the world’s successful speech solutions today have Nuance speech technology inside.

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Speech recognition

Our goal: Near-perfect speech recognition for everybody in the world.
Nuance has been a pioneer in speech and language technologies for more than 30 years. Our data centers host billions of speech transactions every month in over 40 languages from hundreds of applications. We invest in research, data security, and privacy. Our researchers, experts in speech recognition, statistical modeling, machine learning and computational linguistics, use our resources to continuously advance what can be done with speech and language technology.

Current applications for consumers and companies.
We optimize our technology for four main application scenarios.

– Our personal assistant solutions enable people to communicate with their devices on human terms. Our systems understand people’s intentions and provide appropriate responses. Drivers operate their GPSs, make phone calls and listen to messages using our robust speech solutions. Speech makes these interactions easier and safer. In that sense, speech technology saves lives. We continuously improve our accuracy, latency, and robustness; and extend our models to new domains, accents, languages, and devices.

– Our document creation solution powers Dragon NaturallySpeaking, the Nuance flagship speech recognition product. We develop a highly personalized speech recognition solution for each user without explicit training. Our solution not only transcribes accurately the words people dictate, but also formats the resulting written documents.

– Medical professionals use our dictation solutions to generate millions of reports every day. We offer both “front-end” solutions, where doctors see and correct reports as they dictate, and “back-end” solutions, where users speak into a microphone, and are later presented with corrected, formatted reports for signature.

– Most spoken communication takes place between people. Our transcription solution accurately converts the spoken words in conversational speech, particularly voicemails, into text. We focus our research on particular challenges in conversational speech, e.g. sloppy formulation and articulation, difficult recording conditions, multiple speakers, and unpredictable content.

Our solutions are implemented in server-based systems, embedded systems, and hybrid systems that use both server and embedded components. We work closely with our hosted operations and frequently roll out new algorithms and models

Where we’re headed next
Here are some representative examples of the problems we research:

  • Acoustic modeling 
    Neural Nets, and particularly “Deep” Neural Nets, provide substantial performance improvements for many speech recognition tasks. Our work around NNs covers network architectures (e.g. DNN, CNN, RNN/LSTM), input features, training algorithms (parallelization, sequence training), and runtime optimizations (and of course other issues). DNNs require well-labeled data and are hard to adapt to new speakers, devices, and acoustic conditions. So we are also interested in using our large corpora of unlabeled data in many languages for DNN training, and in rapidly adapting DNNs for new acoustic conditions.
  • Language modeling
    Recent years have witnessed a loosening of the death-grip of Kneser-Ney (KN) NGram models on state of the art language modeling, with exponential class models (aka Model M) and more recently various large-scale continuous space language models (aka Feed-Forward NN, RNN, LSTM) achieving superior perplexity and word error rate performance over a range of tasks. The recurrent version of these neural models drop the long-held NGram Markov approximation altogether. The gains in performance with these “new” models come with the cost of a significant increase in training times as compared to KN models. Meanwhile cloud-based dictation services have opened the floodgates of (unsupervised) in-domain training data, which KN models are only too happy to consume and benefit from. This presents an interesting challenge: what decisions about model architecture, training implementation and infrastructure (e.g. CPU, GPU, CPU cluster, multiple GPUs), objective function, parameter initialization, optimization method, data selection, and model combination lead to the best performing model within a practical timeframe robustly across application domains? When theory and engineering collide, the most interesting problems are born. We’re having fun solving these problems every day (but don’t worry, there are still a few left).
  • Research engineering
    The research engineering department’s goal is to turn complex algorithms into efficient, robust software. We create and maintain well-engineered toolkits that support researchers’ flexibility to create and test new algorithms, and we create engines and models which support products and applications in over 40 languages. Our customers range from a single researcher trying something new, to millions of users running Nuance products on their own devices or recognition services provided by our data centers. Our model training toolkit runs on our dedicated large-scale computing grid, and our engines run on anything from the smallest devices to large cloud servers.

Explore recent publications by Nuance Speech Recognition researchers.

Selected articles

Large scale hierarchical neural network language models

Feed-forward neural network language models (NNLMs) are known to improve both perplexity and word error rate performance for speech recognition compared with conventional ngram language

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A hierarchical Bayesian approach for semi-supervised discriminative language modeling

Discriminative language modeling provides a mechanism for differentiating between competing word hypotheses, which are usually ignored in traditional maximum likelihood estimation of N-gram language models.

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Comparison of grapheme-to-phoneme methods on large pronunciation dictionaries and lvcsr tasks

Grapheme-to-Phoneme conversion (G2P) is usually used within every state-of-the-art ASR system to generalize beyond a fixed set of words. Although the performance is typically already

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Unsupervised latent speaker language modeling.

In commercial speech applications, millions of speech utterances from the field are collected from millions of users, creating a challenge to best leverage the user data

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Non-intrusive speech intelligibility assessment

We present NISI, a novel non-intrusive speech intelligibility assessment method based on feature extraction and a binary tree regression model. A training method using the

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Investigation on cross- and multilingual mlp features under matched and mismatched acoustical conditions

In this paper, Multi Layer Perceptron (MLP) based multilingual bottleneckfeatures are investigated for acoustic modeling in three languages— German, French, and US English. We use

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Free English and Czech telephone speech corpus shared under the CC-BY-SA 3.0 license

We present a dataset of telephone conversations in English and Czech, developed to train acoustic models for automatic speech recognition (ASR) in spoken dialogue systems

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Deep neural network trained with speaker representation for speaker normalization

A method for speaker normalization in deep neural network (DNN) based discriminative feature estimation for automatic speech recognition (ASR) is presented. This method is applied

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Child automatic speech recognition for US English: child interaction with living-room-electronic-devices

Adult-targeted automatic speech recognition (ASR) has made significant advancements in recent years and can produce speech-to-text output with very low word-error-rate, for multiple languages, and

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