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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

Modeling Pronunciation Variations for Automatic Speech Recognition with Context-Dependent Acoustic Models

Previous work on data-driven modeling of pronunciation variations for Automatic Speech Recognition demonstrated significant gains in recognition accuracy for systems working with context-independent (CI) phoneme

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Development of a phoneme-to-phoneme (p2p) converter to improve the grapheme-to-phoneme(g2p) conversion of names

It is acknowledged that a good phonemic transcription of proper names is imperative for the success of many modern speech-based services such as directory assistance, car

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Pronunciation Variation Modeling for ASR: Large Improvements are Possible but Small Ones are Likely to Achieve

In this paper a previously proposed method for the automatic construction of a lexicon with pronunciation variants for ASR is further developed and evaluated. The basic idea

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On the Importance of Exception and Cross-word Rules for the Data-driven Creation of Lexica for ASR

Based on earlier work, we developed a new data-driven approach for building a lexicon with multiple pronunciation variants per word. The method automatically learns stochastic pronunciation

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Data-Driven Lexical Modeling of Pronunciation Variations for ASR

In this paper a method for the automatic construction of a lexicon with multiple entries per word is described. The basic idea is to transform

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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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Social Signal Classification Using Deep BLSTM Recurrent Neural Networks

Non-verbal speech cues play an important role in human communication such as expressing emotional states or maintaining the conversational flow. In this paper we investigate

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Hierarchical neural networks and enhanced class posteriors for social signal classification

With the impressive advances of deep learning in recent yearsthe interest in neural networks has resurged in the fields ofautomatic speech recognition and emotion recognition.

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Likability Classification – A Not So Deep Neural Network Approach

This papers presents results on the application of restricted Boltzmann machines (RBM) and deep belief networks(DBN) on the Likability Sub-Challenge of the Interspeech 2012 Speaker

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Comparing Linear Feature Space Transformations for Correlated Features

In automatic speech recognition, a common method to decorrelate features and to reduce feature space dimensionality is Linear Discriminant Analysis (LDA). In this paper, the performance of

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