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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 continuously expand our research grid to explore this avalanche of data. Our researchers, experts in the fields of speech recognition, statistical modeling, deep machine learning, and linguistics, use these computational and data resources to continuously advance the boundaries of what can be done with speech 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

A single-channel non-intrusive C50 estimator correlated with speech recognition performance

Abstract—Several intrusive measures of reverberation can be computed from measured and simulated room impulse responses, over the full frequency band or for each individual mel-frequency

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Adieu Features? End-to-end speech emotion recognition using a deep convolutional recurrent network

The automatic recognition of spontaneous emotions from speech is a challenging task. On the one hand, acoustic features need to be robust enough to capture

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Statistical Signal Processing Techniques for Robust Speech Recognition

Automatic speech recognition is becoming increasingly more important, with commercial applications such as call steering, dictation or voice-enabled personal assistance systems. Although successful in many

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Speech Enhancement with LSTM Recurrent Neural Networks and its Application to Noise-Robust ASR

We evaluate some recent developments in recurrent neural network (RNN) based speech enhancement in the light of noise-robust automatic speech recognition (ASR). The proposed framework

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Situation-aware message presentation for automotive messaging

A text message processing arrangement is described for use in a mobile environment. A mobile messaging application processes user text messages during a user messaging

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Improved strategies for a zero OOV rate LVCSR system

In this work, multiple hierarchical language modeling strategies for a zero OOV rate large vocabulary continuous speech recognition system are investigated. In our previously proposed

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Be at Odds? – Deep and Hierarchical Neural Networks for Classification and Regression of Conflict in Speech

Conflict is a fundamental phenomenon inevitably arising in inter-human communication and only recently has become the subject of study in the emerging field of computational

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Semi-supervised Chinese word segmentation using partial-label learning with conditional random fields

There is rich knowledge encoded in online web data. For example, punctuation and entity tags in Wikipedia data define some word boundaries in a sentence.

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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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Discriminative Bernoulli mixture models for handwritten digit recognition

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