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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

Speech recognition with dynamic grammars using finite-state transducers.

Spoken language systems, ranging from interactive voice response (IVR) to mixed-initiative conversational systems, make use of a wide range of recognition grammars and vocabularies.  The

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Probabilistic language modeling with left corner parsing
N-Best Rescoring Based on Pitch-accent Patterns

In this paper, we adopt an n-best rescoring scheme using pitch-accent patterns to improve automatic speech recognition (ASR) performance. The pitch-accent model is decoupled from

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Automatic prosodic event detection using a novel labeling and selection method in co-training

Most previous approaches to automatic prosodic event detection are based on supervised learning, relying on the availability of a corpus that is annotated with the

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Use of acoustic prior information for confidence measure in asr Applications

In this paper, we propose a new acoustic confidence measure of ASR hypothesis and compare it to approaches proposed in the literature. This approach takes

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Model-based independent component analysis for robust multi-microphone automatic speech recognition

In this communication, we present a method for noise-robust multi-microphone automatic speech recognition (ASR). It is assumed that the speech source to be recognized is

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Phonetic alignment: speech synthesis-based vs. Viterbi-based.

In this paper we compare two different methods for automatically phonetically labeling a continuous speech database, as usually required for designing a speech recognition or

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Robust feature extraction and acoustic modeling at Multitel: experiments on the Aurora databases

This paper intends to summarize some of the robust feature extraction and acoustic modeling technologies used at Multitel, together with their assessment on some of

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An architecture for voice-enabled interfaces over local wireless networks

Lots of industrial tasks need contacts between operators and a central Information Management System. Permanent contact cre­ates a more effective and efficient workforce with a

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Combined use of close-talk and throat microphones for improved speech recognition under non-stationary background noise.

This paper intends to summarize recent developments and ex­perimental results related to Automatic Speech Recognition (ASR) using signals captured with a throat-microphone. Due to the

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