This study reveals that the magnetotelluric (MT) method can detect the responses of the ionospheric E-region to the annual solar eclipse on June 21, 2020. The MT method not only detected a significant reduction in the solar quiet current by approximately 3.3 h over Lijiang during the obscuration but also, for the first time, observed waves with periods ranging from a few to dozens of minutes in the electric field due to the eclipse. The evolution of electric and magnetic fields suggests nonstationary changes in the ionospheric E-region structures during and after the eclipse.

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve sub-systems and their fusion submitted to SRE'18. It is also our intention to present a shared view on the advancements, progresses, and major paradigm shifts that we have witnessed as an SRE participant in the past decade from SRE'08 to SRE'18. In this regard, we have seen, among others, a paradigm shift from supervector representation to deep speaker embedding, and a switch of research challenge from channel compensation to domain adaptation.

In this paper, we study the role of long-time analytic phase of speech signals in spoken language recognition (SLR) and employ a set of features termed as instantaneous frequency cepstral coefficients (IFCC). We extract IFCC from long-time analytic phase, in an effort to capture long range acoustic features from speech signals. These features are used in combination with the traditional shifted delta cepstral coefficients (SDCC) for SLR. As the SDCC are extracted from spectral magnitude and IFCC are from analytic phase, they characterize long-time information of speech in different ways. The experiments conducted with NIST LRE 2017 task reveals the complementary effects of IFCC features to SDCC and deep bottleneck (DBN) features. The fusion of IFCC with SDCC/DBN features delivered relative improvements of 23.23% and 16.78% in average equal error rate over the SDCC and DBN features, respectively, indicating the benefits of information from analytic phase in SLR.

18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017, Stockholm, Sweden, 20-24 August 2017

Technical report for NIST LRE 2015 Workshop

This paper presents a new approach and the study of GMM-SVM system for text-dependent speaker recognition on scenario of the fixed pass-phrases. The uniform-split content-based GMM-SVM system is proposed and applied to text-dependent speaker evaluation. We conducted detailed study of the proposed method compared to the baseline GMM-SVM system on the RSR2015 database, which has been designed and collected for the evaluation of text-dependent speaker verification system. The experiment results show that the new approach can significantly reduce the detection error of the target-wrong error type (i.e., target speaker with wrong pass-phrase) while maintaining a low detection error for both imposter-correct and imposter-wrong error types (i.e., imposter with correct pass-phrase and imposter with wrong pass-phrase). We also show that score normalization could be applied with respect to the imposter-wrong distribution as opposed to the imposter-correct distribution.

This paper presents an anti-model based speaker recognition system for NIST SRE 2012 evaluation, which is one of subsystems in IIR SRE12 submission. We apply the anti-model approach for the SRE12 evaluation. The KL-SVM-NAP based speaker recognition system is adopted to evaluate the performance. We present detailed comparison study of the classical KL-SVM-NAP based speaker recognition system and anti-model based KL-SVM-NAP system for NIST 2012 speaker recognition evaluation. The results are reported on in-house pre-SRE12 development set and NIST SRE12 core task. The clear advantages of the anti-model approach over that the traditional KL-SVM-NAP approach are presented and discussed.

I4U is a joint entry of nine research Institutes and Universities across 4 continents to NIST SRE 2012. It started with a brief discussion during the Odyssey 2012 workshop in Singapore. An online discussion group was soon set up, providing a discussion platform for different issues surrounding NIST SRE’12. Noisy test segments, uneven multi-session training, variable enrollment duration, and the issue of open-set identification were actively discussed leading to various solutions integrated to the I4U submission. The joint submission and several of its 17 sub-systems were among top-performing systems. We summarize the lessons learnt from this large-scale effort.

This paper presents a design strategy for the speaker diarization system in the IIR submissions to the 2007 and 2009 NIST Rich Transcription Meeting Recognition Evaluations (RT07 and RT09) for the multiple distant microphone (MDM) condition. The system features two algorithms supporting two important steps in a diarization process. The first step is Initial Segmentation and Clustering (ISC), and the second one is cluster merging and purification. In the ISC step, we propose a histogram quantization and clustering technique based on time delay of arrival (TDOA) features by analyzing the correlation among the signals across multiple distant microphones. In the cluster merging and purification step, we further merge the speaker clusters using a Bayesian information criterion (BIC) to consolidate the clusters to arrive at one-cluster-per-speaker. The two steps work in tandem to form an integral process. We propose a novel Consensus Based Cluster Purification (CBCP) method that involves a technique to remove impure speaker segments in the speaker clusters before speaker modeling in the cluster purification process. The system reports a state-of-the-art performance of speaker diarization for RT07 and RT09 MDM condition with 7.47% and 8.77% Diarization error rates (DERs), respectively, for both overlapping and non-overlapping speech.

This paper describes an improved speaker diarization system for the Single Distant Microphone (SDM) task in the 2007 and 2009 NIST Rich Transcription Meeting Recognition Evaluations. The system includes three main modules: front-end processing, initial speaker clustering and cluster purification/merging. The front-end processing involves the Wiener filtering for the targeted audio channels and a self-adaptation speech activity detection algorithm. A simple but effective energy based segmentation is applied to chunk the meeting data into small segments to construct the initial clusters. An enhanced purification algorithm is proposed to further improve the performance after the preliminary purification, and the BIC criterion is adopted for the cluster merging. The system achieves competitive overall DERs of 15.67% for RT07 SDM speaker diarization task and 17.34% for RT09 SDM speaker diarization task.

We propose an acoustic segment model (ASM) approach to incorporating temporal information into speaker modeling in text-independent speaker recognition. In training, the proposed framework first estimates a collection of ASM-based universal background models (UBMs). Multiple sets of speaker-specific ASMs are then obtained by adapting the ASM-based UBMs with speaker-specific enrollment data. A novel usage of language models of the ASM units is also proposed to characterize transitions among ASMs. In the testing phase the ASM sets for the claimed speaker and UBMs, along with a bigram ASM language model, are used to calculate detection scores for each given test utterance. We report on speaker recognition experiments using the NIST 2001 SRE database. The results clearly indicate that the proposed ASM-based method achieves a notable improvement over the GMM-based speaker modeling in which no temporal modeling is considered. Moreover, a further error reduction is obtained by integrating the language model, another inclusion of temporal properties made possibly by ASM based speaker modeling.

This paper presents new techniques for front-end analysis using long-term and temporal information for speaker recognition. We propose a long-term feature analysis strategy that averages short-time spectral features over a period of time in an effort to capture the speaker traits that are manifested over a speech segment longer than a spectral frame. We found that the moving averages of temporal information are effective in speaker recognition as well. The experiments on the 2008 NIST Speaker Recognition Evaluation dataset show the longterm and temporal information contribute to substantial EER reductions.

This paper reports the IIR speaker recognition system for the summed channel evaluation tasks in the NIST SRE 2008 and 2010. The system includes three main modules: voice activity detection, speaker diarization and speaker recognition. The front-end process employs a voice activity detection algorithm for effective speech frame selection. The speaker diarization system that was developed for 2007 and 2009 NIST RT Evaluations is adopted for summed channel speech segmentation. A hybrid purifying and clustering algorithm is developed to segregate the summed channel speech by speakers. The GMM-SVM speaker recognition system is adopted to evaluate the performance with both MFCC and LPCC features. The system achieves an overall EER of 3.46% in the 1conv-summed task and 1.87% in the 8conv-summed task, respectively, where only all English trials are involved.

This paper describes the performance of the I4U speaker recognition system in the NIST 2008 Speaker Recognition Evaluation. The system consists of seven subsystems, each with different cepstral features and classifiers. We describe the I4U Primary system and report on its core test results as they were submitted, which were among the best-performing submissions. The I4U effort was led by the Institute for Infocomm Research, Singapore (IIR), with contributions from the University of Science and Technology of China (USTC), the University of New South Wales, Australia (UNSW), Nanyang Technological University, Singapore (NTU) and Carnegie Mellon University, USA (CMU).

This paper describes speaker diarization system on a NIST Rich Transcription 2007 (RT-07) meeting recognition evaluation data set for the task of multiple distant microphone (MDM). Our implementation includes three components: initial clustering, non-speech removal and cluster purification. Initial clusters are generated using directional of arrival (DOA) information and bootstrap clustering. Multiple GMM modeling for speech/non-speech classification is employed for non-speech removal component. In addition, a novel system fusion strategy using information from receiver operating curve (ROC) is proposed for non-speech removal component. Finally, consensus clustering approach together with iterative GMM clustering method is employed for speaker cluster purification. The system achieves the overall DER of 10.81%.

In this paper, we present a pseudo-key analysis approach for cross-validation of language recognition systems before the ground truth (true key) becomes available. A state-of-the-art language recognition system typically employs multiple language recognition classifiers which are fused to form a mixture of experts. The individual classifiers are also called subsystems. To avoid the fused system from being brought down by some outlier classifiers, pseudo keys are designed to cross-examine the integrity of individual classifier candidates. The language recognition experiments are conducted on the NIST 2007 Language Recognition Evaluation (LRE) corpus using the subsystems in the primary submission from the Institute for Infocomm Research (IIR).

This paper describes a speaker diarization system in 2007 NIST Rich Transcription (RT07) Meeting Recognition Evaluation for the task of Multiple Distant Microphone (MDM) in meeting room scenarios. The system includes three major modules: data preparation, initial speaker clustering and cluster purification/merging. The data preparation consists of the raw data Wiener filtering and beamforming, Time Difference of Arrival estimate and speech activity detection. Based on the initial processed data, two-stage histogram quantization has been used to perform the initial speaker clustering. A modified purification strategy via high-order GMM clustering method is proposed. BIC criterion is applied for cluster merging. The system achieves a competitive overall DER of 8.31% for RT07 MDM speaker diarization task.

This paper presents a statistical analysis of a modified adaptive filter-bank when applied to the estimation of harmonic signals. The application of such a filter-bank for the tracking of the fundamental frequency, estimation of the amplitude and phase of each harmonic component is investigated under various conditions: different A/D quantization errors, the fundamental frequency having small time-varying variations and, finally, mismatches in the filter-bank lengths to the number of the harmonics. The analysis results indicate that this filter-bank is very robust for the estimation of amplitude and phase of harmonic signals.

A new type of implicit bit allocation (IBA) is studied for the combined coarse granular scalability (CGS) and spatial scalability. Given a region which is a quality level at a spatial resolution and an input of weighting factor in the region that is determined by customers' interest, the IBA is formulated as a multiple objective optimization problem. The IBA exhibits a distinguished feature that allows bits allocation to each region being fixed and only tradeoff between motion information and residual information in each region can be properly set such that coding efficiency of each region is guaranteed in order according to the weighting factor. Due to the nonlinearity of the combined CGS and spatial scalability, this optimization problem is very complex. In this paper, a simple solution to the optimization problem is proposed by using the conventional approach of "Divide and Conquer" or "Hierarchy." Two cross-layer motion estimation/motion compensation (ME/MC) schemes that were introduced for the CGS and spatial scalability are further adopted to support the solution. It is shown that a combined CGS and spatial scalability scheme together with adaptability to customer composition allows the solution to achieve a customer oriented scalable tradeoff (COST)

In this paper, a microphone array with 3-D focal zone is proposed. The microphone array consists of one omni-directional and two uni-directional microphones. The microphone array is so constructed that a cross zone is formed such that only the sound within this zone is captured and any interferences outside the zone are effectively cancelled. The proposed framework is flexible in defining the location/size of the closed volume where the sound source of interest is located. Simulations have been carried out to demonstrate the 3-D spatial selectivity as well as the noise cancellation performance. The most important feature which differs from the previous works is that the super volumetric selectivity is realized by strategically use only three microphones, by which the overall apparatus acts as a virtual wireless close-talking microphone with confined position constrained in both distance and directions.

We compare and evaluate the various noise cancellation schemes available in what we term 'dual microphone systems' (DMS). A DMS is a directional audio-capturing device consisting of two microphones with possibly different directional characteristics: omnidirectional; bidirectional; cardioid. A general structure is proposed to represent coherently the different schemes for the DMS. This is followed by a theoretical derivation of the performance of various DMS configurations under incoherent, coherent and diffuse noise fields. The relationships between the different configurations and some guidelines for designing DMS are also presented.

In this paper, an investigation to establish a possible relationship between the performance of a telephony speech recognition system and the method for objective speech quality assessment described in ITU-T Recommendation P.862, known as Perceptual Evaluation of Speech Quality (PESQ), is presented. Experiments using various additive background noises, and at different separations between the microphone and the sound-source have been conducted to establish such a relationship. The preliminary results suggest that telephony speech recognition rates can be mapped to the mean opinion score (MOS) obtained by PESQ using a relatively simple polynomial relationship. This indicates that the PESQ MOS can act as a reliable predictor for the achievable speech recognition rates for telephony-based speech recognition systems.

In this paper, we compare and evaluate the various noise cancellation schemes available in what we term a dual microphone system. A dual microphone system (DMS) is a composite directional audio-capturing device which consists of two microphones, each microphone having possibly different directional characteristics, e.g., omnidirectional, bidirectional or cardioid. By recasting the various combinations of two microphones for a DMS into a coherent and familiar framework of generalized sidelobe canceller (GSC), we subsequently derive the expected noise reduction of various structures under incoherent, coherent and diffuse noise fields, followed by a series of experiments in a typical office environment. These results are indicative of the achievable reduction of noise in real applications. The relationship and differences between the various methods are also discussed

A pseudo adaptive microphone array scheme is presented in this paper for high quality speech acquisition. This two-stage scheme consists of an initial enhanced cross-power spectrum phase (ECPSP) stage, which is used to estimate the directions of the target as well as the spatially distinguishable undesired signals, followed by an optimum spatial filter, constructed from the orthogonal components of the interferer subspace. The proposed method may thus be able to minimize the energy in the estimated directions corresponding to the interferers. Since the spatial filter is obtained solely from the estimated directions of the interferers, some difficulties encountered in adaptive beamforming technology, e.g., the negative impact of reverberation, stability issues in adaptive control in the case of multiple or nonstationary interferers, target-signal canceling and distortion, can be avoided to some extent. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.