The United States Secret Service (USSS) teamed with MIT Lincoln Laboratory (MIT/LL) in the US National Institute of Standards and Technology's 2010 Speaker Recognition Evaluation of Human Assisted Speaker Recognition (HASR). We describe our qualitative and automatic speaker comparison processes and our fusion of these processes, which are adapted from USSS casework. The USSS-MIT/LL 2010 HASR results are presented. We also present post-evaluation results. The results are encouraging within the resolving power of the evaluation, which was limited to enable reasonable levels of human effort. Future ideas and efforts are discussed, including new features and capitalizing on naive listeners.

Regions of nonmodal phonation, exhibiting deviations from uniform glottal-pulse periods and amplitudes, occur often and convey information about speaker- and linguistic-dependent factors. Such waveforms pose challenges for speech modeling, analysis/synthesis, and processing. In this paper, we investigate the representation of nonmodal pulse trains as a sum of harmonically-related sinewaves with time-varying amplitudes, phases, and frequencies. We show that a sinewave representation of any impulsive signal is not unique and also the converse, i.e., frame-based measurements of the underlying sinewave representation can yield different impulse trains. Finally, we argue how this ambiguity may explain addition, deletion, and movement of pulses in sinewave synthesis and a specific illustrative example of time-scale modification of a nonmodal case of diplophonia. Index Terms: sinewave analysis, sinewave synthesis, nonmodal phonation

In this paper we present a new adaptive short-time Fourier analysis-synthesis scheme and demonstrate its efficacy in speech enhancement. While a number of adaptive analyses have previously been proposed to overcome the limitations of fixed-resolution schemes, we propose here a modified overlap-add procedure that enables efficient resynthesis. Our adaptation scheme extends earlier work using local measures of time-frequency concentration, and is applicable to power spectral density estimation for the case of noisy speech. We provide evidence of increased gains in signal-to-noise ratios for synthetic signals as well as empirical evidence of reduced musical noise based on expert listening tests for voiced and phonetically balanced utterances observed in noise, relative to a standard baseline speech enhancement system whose time-frequency resolution is fixed.

A multimodal person recognition architecture has been developed for the purpose of improving overall recognition performance and for addressing channel-specific performance shortfalls. This multimodal architecture includes the fusion of a face recognition system with the MIT/LL GMM/UBM speaker recognition architecture. This architecture exploits the complementary and redundant nature of the face and speech modalities. The resulting multimodal architecture has been evaluated on the XM2VTS corpus using the Lausanne open set verification protocols, and demonstrates excellent recognition performance. The multimodal architecture also exhibits strong recognition performance gains over the performance of the individual modalities.

One particularly difficult challenge for cross-channel speaker verification is the auxiliary microphone task introduced in the 2005 and 2006 NIST Speaker Recognition Evaluations, where training uses telephone speech and verification uses speech from multiple auxiliary microphones. This paper presents two approaches to compensate for the effects of auxiliary microphones on the speech signal. The first compensation method mitigates session effects through Latent Factor Analysis (LFA) and Nuisance Attribute Projection (NAP). The second approach operates directly on the recorded signal with noise reduction techniques. Results are presented that show a reduction in the performance gap between telephone and auxiliary microphone data.

The area of automatic speaker recognition has been dominated by systems using only short-term, low-level acoustic information, such as cepstral features. While these systems have produced low error rates, they ignore higher levels of information beyond low-level acoustics that convey speaker information. Recently published works have demonstrated that such high-level information can be used successfully in automatic speaker recognition systems by improving accuracy and potentially increasing robustness. Wide ranging high-levelfeature-based approaches using pronunciation models, prosodic dynamics, pitch gestures, phone streams, and conversational interactions were explored and developed under the SuperSID

In this paper, an adaptive approach to the enhancement of speech signals is developed based On auditory spectral change. The algorithm is motivated by sensitivity of aural, biologic systems to signal dynamics, by evidence that noise is aurally masked by rapid changes in a signal, and by analogies to these two aural phenomena in biologic visual processing. Emphasis is on preserving nonstationarity, i.e., speech transient and time-varying components, such as plosive bursts, formant transitions, and vowel onsets, while suppressing additive noise. The essence of the enhancement technique is a Wiener :filter that uses a desired signal spectrum whose estimation adapts to stationarity of the measured signal. The degree of stationarity is derived from a signal change measurement, based on an auditory spectrum that accentuates change in spectral bands, The adaptive filter is applied in an unconventional overlap-add analysis/synthesis framework, using a very short 4-ms analysis Window and a 1-ms frame interval. In informal listening the reconstructions are judged to be “crisp” corresponding to good temporal resolution of transient and rapidly-moving speech events.

In this paper we present an approach to close the gap between text-dependent and text-independent speaker verification performance. Text-constrained GMM-UBM systems are created using word segmentations produced by a LVCSR system on conversational speech allowing the system to focus on speaker differences over a constrained set of acoustic units. Results on the 2001 NIST extended data task show this approach can be used to produce an equal error rate of < 1 %.

This paper describes an embedded sinusoidal transform codec that is scalable not only in bit-rate, but also in sampling rate. In a representative implementation, the system produces an embedded bit-stream at 3.2 and 6.4 kbit/s for telephone-bandwidth speech, and scales up to 9.6 kbit/s for 16 kHz sampled wideband speech. The 3.2 kbit/s codec is a sinusoidal transform codec with synthetic phases. The 6.4 kbit/s codec adds resolution to the spectral envelope and transmits measured phases of the eight lowest harmonics. The 9.6 kbit/s codec adds information in the 4 to 8 kHz band to provide higher quality wideband speech.

Experiments in Gaussian-mixture-model speaker recognition from mel-cepstra, derived from mel-filter bank energies (MFBs) of the G.729 codec all-pole spectral envelope, showed significant performance loss relative to the standard mel-cepstral coefficients of G.729 synthesized (coded) speech (Quatieri et al. 1999). In this paper, we investigate two approaches to recover speaker recognition performance from G.729 parameters. The first is a parametric approach that makes explicit use of G.729 parameters, rather than deriving cepstra from MFBs of an all-pole spectrum. Specifically, the G.729 LSFs are converted to "direct" cepstral coefficients for which there exists a one-to-one correspondence with the LSFs. The G.729 residual is also considered; in particular, appending G.729 pitch as a single parameter to the direct cepstral coefficients gives further performance gain. The second nonparametric approach uses the original MFB paradigm, but adds harmonic striations to the G.729 all-pole spectral envelope. Although obtaining considerable performance gains with these methods, we have yet to match the performance of G.729 synthesized speech, motivating the need for representing additional fine structure of the G.729 residual.

This paper proposes our new text-to-speech (TTS) system that concatenates large numbers of speech segments to produce very natural and intelligible synthetic speech. One novel point of our system is its new synthesis unit, which is has three remarkable characteristics as follows; The synthesis units contain all Japanese syllables together with all possible vowel sequences, so very smooth synthetic speech is produced. Both previous and succeeding phoneme environments are considered when speech segments are concatenated, so natural sounding transients from a vowel to a consonant, which is the only concatenation point with the proposed unit, are present in the synthetic speech. Each unit has various fundamental frequency (F0) contours. Therefore, F0 modification rates are very small in any synthesis event, and the F0 modification process causes only minor distortion. To develop a unit database efficiently and effectively, we analyzed 4,850,000 Japanese phrases (breath-group) containing 87,810,000 phonemes and ranked them in order of appearance frequency. Listening tests confirm the high intelligibility and naturalness of speech produced by our new TTS system. It uses the 50,000 highest frequency units that cover over 77% of Japanese texts.

A dual rate embedded sinusoidal transform coder is described in which a core 14th order allpole coder operating at 2400 b/s is augmented with a set of channel gain residuals in order to operate at the higher 4800 b/s rate. The channel. Gains are a set of non-uniformly spaced samples of the spline envelope and constitute a lowpass estimate of the short-time vocal tract magnitude spectrum. The channel gain residuals represent the difference between the spline envelope and the quantized 14th order allpole spectrum at the channel gain frequencies. The channel gain residuals are coded using pitch dependent scalar quantization. Informal listening indicates that the quality of the embedded coder at 4800 b/s is comparable to that of an existing high quality 4800 b/s allpole coder.

A new approach to time-scale expansion of short-duration complex acoustic signals is introduced. Using a subband signal representation, channel phases are selected to preserve a desired time-scaled temporal envelope. The phase representation is derived from locations of events that occur within filter bank outputs. A frame-based generalization of the method imposes phase consistency across consecutive synthesis frames. The method is applied to synthetic and actual complex acoustic signals consisting of closely spaced rapidly damped sine waves. Time-frequency resolution limitations are discussed.