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Cognitive services for the user

January 1, 2009
  |
Book Chapter
Author:
Joseph P. Campbell Jr
…
Published in:
Chapter 10, Cognitive Radio Technology, 2009, pp. 305-324.
Topic:
biometrics
R&D area:
R&D group:

Summary

Software-defined cognitive radios (CRs) use voice as a primary input/output (I/O) modality and are expected to have substantial computational resources capable of supporting advanced speech- and audio-processing applications. This chapter extends previous work on speech applications (e.g., [1]) to cognitive services that enhance military mission capability by capitalizing on automatic processes, such as speech information extraction and understanding the environment. Such capabilities go beyond interaction with the intended user of the software-defined radio (SDR) - they extend to speech and audio applications that can be applied to information that has been extracted from voice and acoustic noise gathered from other users and entities in the environment. For example, in a military environment, situational awareness and understanding could be enhanced by informing users based on processing voice and noise from both friendly and hostile forces operating in a given battle space. This chapter provides a survey of a number of speech- and audio-processing technologies and their potential applications to CR, including: - A description of the technology and its current state of practice. - An explanation of how the technology is currently being applied, or could be applied, to CR. - Descriptions and concepts of operations for how the technology can be applied to benefit users of CRs. - A description of relevant future research directions for both the speech and audio technologies and their applications to CR. A pictorial overview of many of the core technologies with some applications presented in the following sections is shown in Figure 10.1. Also shown are some overlapping components between the technologies. For example, Gaussian mixture models (GMMs) and support vector machines (SVMs) are used in both speaker and language recognition technologies [2]. These technologies and components are described in further detail in the following sections. Speech and concierge cognitive services and their corresponding applications are covered in the following sections. The services covered include speaker recognition, language identification (LID), text-to-speech (TTS) conversion, speech-to-text (STT) conversion, machine translation (MT), background noise suppression, speech coding, speaker characterization, noise management, noise characterization, and concierge services. These technologies and their potential applications to CR are discussed at varying levels of detail commensurate with their innovation and utility.
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Summary

Software-defined cognitive radios (CRs) use voice as a primary input/output (I/O) modality and are expected to have substantial computational resources capable of supporting advanced speech- and audio-processing applications. This chapter extends previous work on speech applications (e.g., [1]) to cognitive services that enhance military mission capability by capitalizing on automatic...

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Cognitive services for the user

Eigen-channel compensation and discriminatively trained Gaussian mixture models for dialect and accent recognition

September 22, 2008
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Conference Paper
Author:
Pedro A. Torres-Carrasquillo
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Published in:
Proc. INTERSPEECH 2008, 22-26 September 2008, pp. 723-726.
Topic:
language recognition
R&D area:
R&D group:

Summary

This paper presents a series of dialect/accent identification results for three sets of dialects with discriminatively trained Gaussian mixture models and feature compensation using eigen-channel decomposition. The classification tasks evaluated in the paper include: 1)the Chinese language classes, 2) American and Indian accented English and 3) discrimination between three Arabic dialects. The first two tasks were evaluated on the 2007 NIST LRE corpus. The Arabic discrimination task was evaluated using data derived from the LDC Arabic set collected by Appen. Analysis is performed for the English accent problem studied and an approach to open set dialect scoring is introduced. The system resulted in equal error rates at or below 10% for each of the tasks studied.
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Summary

This paper presents a series of dialect/accent identification results for three sets of dialects with discriminatively trained Gaussian mixture models and feature compensation using eigen-channel decomposition. The classification tasks evaluated in the paper include: 1)the Chinese language classes, 2) American and Indian accented English and 3) discrimination between three Arabic...

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Eigen-channel compensation and discriminatively trained Gaussian mixture models for dialect and accent recognition

The MITLL NIST LRE 2007 language recognition system

September 22, 2008
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Conference Paper
Author:
Pedro A. Torres-Carrasquillo
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Published in:
INTERSPEECH 2008, 22-26 September 2008, 719-722.
Topic:
language recognition
R&D area:
R&D group:

Summary

This paper presents a description of the MIT Lincoln Laboratory language recognition system submitted to the NIST 2007 Language Recognition Evaluation. This system consists of a fusion of four core recognizers, two based on tokenization and two based on spectral similarity. Results for NIST?s 14-language detection task are presented for both the closed-set and open-set tasks and for the 30, 10 and 3 second durations. On the 30 second 14-language closed set detection task, the system achieves a 1% equal error rate.
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Summary

This paper presents a description of the MIT Lincoln Laboratory language recognition system submitted to the NIST 2007 Language Recognition Evaluation. This system consists of a fusion of four core recognizers, two based on tokenization and two based on spectral similarity. Results for NIST?s 14-language detection task are presented for...

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The MITLL NIST LRE 2007 language recognition system

Beyond frame independence: parametric modelling of time duration in speaker and language recognition

September 22, 2008
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Conference Paper
Author:
Alan V. McCree
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Published in:
INTERSPEECH 2008, 22-26 September 2008, pp. 767-770.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

In this work, we address the question of generating accurate likelihood estimates from multi-frame observations in speaker and language recognition. Using a simple theoretical model, we extend the basic assumption of independent frames to include two refinements: a local correlation model across neighboring frames, and a global uncertainty due to train/test channel mismatch. We present an algorithm for discriminative training of the resulting duration model based on logistic regression combined with a bisection search. We show that using this model we can achieve state-of-the-art performance for the NIST LRE07 task. Finally, we show that these more accurate class likelihood estimates can be combined to solve multiple problems using Bayes' rule, so that we can expand our single parametric backend to replace all six separate back-ends used in our NIST LRE submission for both closed and open sets.
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Summary

In this work, we address the question of generating accurate likelihood estimates from multi-frame observations in speaker and language recognition. Using a simple theoretical model, we extend the basic assumption of independent frames to include two refinements: a local correlation model across neighboring frames, and a global uncertainty due to...

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Beyond frame independence: parametric modelling of time duration in speaker and language recognition

Multisensor very low bit rate speech coding using segment quantization

March 31, 2008
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Conference Paper
Author:
Alan V. McCree
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 31 March - 4 April 2008, pp. 3997-4000.
Topic:
speech processing
R&D area:
R&D group:

Summary

We present two approaches to noise robust very low bit rate speech coding using wideband MELP analysis/synthesis. Both methods exploit multiple acoustic and non-acoustic input sensors, using our previously-presented dynamic waveform fusion algorithm to simultaneously perform waveform fusion, noise suppression, and crosschannel noise cancellation. One coder uses a 600 bps scalable phonetic vocoder, with a phonetic speech recognizer followed by joint predictive vector quantization of the error in wideband MELP parameters. The second coder operates at 300 bps with fixed 80 ms segments, using novel variable-rate multistage matrix quantization techniques. Formal test results show that both coders achieve equivalent intelligibility to the 2.4 kbps NATO standard MELPe coder in harsh acoustic noise environments, at much lower bit rates, with only modest quality loss.
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Summary

We present two approaches to noise robust very low bit rate speech coding using wideband MELP analysis/synthesis. Both methods exploit multiple acoustic and non-acoustic input sensors, using our previously-presented dynamic waveform fusion algorithm to simultaneously perform waveform fusion, noise suppression, and crosschannel noise cancellation. One coder uses a 600 bps...

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Multisensor very low bit rate speech coding using segment quantization

Low-bit-rate speech coding

May 1, 2007
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Book Chapter
Author:
Alan V. McCree
Published in:
Chapter 16 in Springer Handbook of Speech Processing and Communication, 2007, pp. 331-50.
Topic:
speech processing
R&D area:
R&D group:

Summary

Low-bit-rate speech coding, at rates below 4 kb/s, is needed for both communication and voice storage applications. At such low rates, full encoding of the speech waveform is not possible; therefore, low-rate coders rely instead on parametric models to represent only the most perceptually relevant aspects of speech. While there are a number of different approaches for this modeling, all can be related to the basic linear model of speech production, where an excitation signal drives a vocal-tract filter. The basic properties of the speech signal and of human speech perception can explain the principles of parametric speech coding as applied in early vocoders. Current speech modeling approaches, such as mixed excitation linear prediction, sinusoidal coding, and waveform interpolation, use more-sophisticated versions of these same concepts. Modern techniques for encoding the model parameters, in particular using the theory of vector quantization, allow the encoding of the model information with very few bits per speech frame. Successful standardization of low-rate coders has enabled their widespread use for both military and satellite communications, at rates from 4 kb/s all the way down to 600 b/s. However, the goal of toll-quality low-rate coding continues to provide a research challenge.
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Summary

Low-bit-rate speech coding, at rates below 4 kb/s, is needed for both communication and voice storage applications. At such low rates, full encoding of the speech waveform is not possible; therefore, low-rate coders rely instead on parametric models to represent only the most perceptually relevant aspects of speech. While there...

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Low-bit-rate speech coding

Multisensor dynamic waveform fusion

April 1, 2007
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Conference Paper
Author:
Alan V. McCree
…
Published in:
Proc. 32nd Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, April 2007, pp. IV-577 - IV-580.
Topic:
speech enhancement
R&D area:
R&D group:

Summary

Speech communication is significantly more difficult in severe acoustic background noise environments, especially when low-rate speech coders are used. Non-acoustic sensors, such as radar sensors, vibrometers, and bone-conduction microphones, offer significant potential in these situations. We extend previous work on fixed waveform fusion from multiple sensors to an optimal dynamic waveform fusion algorithm that minimizes both additive noise and signal distortion in the estimated speech signal. We show that a minimum mean squared error (MMSE) waveform matching criterion results in a generalized multichannel Wiener filter, and that this filter will simultaneously perform waveform fusion, noise suppression, and crosschannel noise cancellation. Formal intelligibility and quality testing demonstrate significant improvement from this approach.
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Summary

Speech communication is significantly more difficult in severe acoustic background noise environments, especially when low-rate speech coders are used. Non-acoustic sensors, such as radar sensors, vibrometers, and bone-conduction microphones, offer significant potential in these situations. We extend previous work on fixed waveform fusion from multiple sensors to an optimal dynamic...

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Multisensor dynamic waveform fusion

Reducing speech coding distortion for speaker identification

September 17, 2006
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Conference Paper
Author:
Alan V. McCree
Published in:
Int. Conf. on Spoken Language Processing, ICSLP, 17-21 September 2006.
Topic:
speech processing
R&D area:
R&D group:

Summary

In this paper, we investigate the degradation of speaker identification performance due to speech coding algorithms used in digital telephone networks, cellular telephony, and voice over IP. By analyzing the difference between front-end feature vectors derived from coded and uncoded speech in terms of spectral distortion, we are able to quantify this coding degradation. This leads to two novel methods for distortion compensation: codebook and LPC compensation. Both are shown to significantly reduce front-end mismatch, with the second approach providing the most encouraging results. Full experiments using a GMM-UBM speaker ID system confirm the usefulness of both the front-end distortion analysis and the LPC compensation technique.
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Summary

In this paper, we investigate the degradation of speaker identification performance due to speech coding algorithms used in digital telephone networks, cellular telephony, and voice over IP. By analyzing the difference between front-end feature vectors derived from coded and uncoded speech in terms of spectral distortion, we are able to...

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Reducing speech coding distortion for speaker identification

A scalable phonetic vocoder framework using joint predictive vector quantization of MELP parameters

May 14, 2006
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Conference Paper
Author:
Alan V. McCree
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Speech and Language Processing, ICASSP, 14-19 May 2006, pp. 705-708.
Topic:
speech processing
R&D area:
R&D group:

Summary

We present the framework for a Scalable Phonetic Vocoder (SPV) capable of operating at bit rates from 300 - 1100 bps. The underlying system uses an HMM-based phonetic speech recognizer to estimate the parameters for MELP speech synthesis. We extend this baseline technique in three ways. First, we introduce the concept of predictive time evolution to generate a smoother path for the synthesizer parameters, and show that it improves speech quality. Then, since the output speech from the phonetic vocoder is still limited by such low bit rates, we propose a scalable system where the accuracy of the MELP parameters is increased by vector quantizing the error signal between the true and phonetic-estimated MELP parameters. Finally, we apply an extremely flexible technique for exploiting correlations in these parameters over time, which we call Joint Predictive Vector Quantization (JPVQ).We show that significant quality improvement can be attained by adding as few as 400 bps to the baseline phonetic vocoder using JPVQ. The resulting SPV system provides a flexible platform for adjusting the phonetic vocoder bit rate and speech quality.
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Summary

We present the framework for a Scalable Phonetic Vocoder (SPV) capable of operating at bit rates from 300 - 1100 bps. The underlying system uses an HMM-based phonetic speech recognizer to estimate the parameters for MELP speech synthesis. We extend this baseline technique in three ways. First, we introduce the...

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A scalable phonetic vocoder framework using joint predictive vector quantization of MELP parameters
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