Overlapped Speech Detection in Multi-Party Meetings

Detection of simultaneous speech in meeting recordings is a difficult problem due both to the complexity of the meeting itself and the environment surrounding it. The system proposes the use of gammatone-like spectrogram-based linear predictor coefficients on distant microphone channel data for overlap detection functions. The framework utilized the Augmented Multiparty Interaction (AMI) conference corpus to assess model performance. The proposed system offers enhancements over base line feature set models for classification

Detection and handling of overlapping speech for speaker diarization

For the last several years, speaker diarization has been attracting substantial research attention as one of the spoken language technologies applied for the improvement, or enrichment, of recording transcriptions. Recordings of meetings, compared to other domains, exhibit an increased complexity due to the spontaneity of speech, reverberation effects, and also due to the presence of overlapping speech. Overlapping speech refers to situations when two or more speakers are speaking simultaneously. In meeting data, a substantial portion of errors of the conventional speaker diarization systems can be ascribed to speaker overlaps, since usually only one speaker label is assigned per segment. Furthermore, simultaneous speech included in training data can eventually lead to corrupt single-speaker models and thus to a worse segmentation. This thesis concerns the detection of overlapping speech segments and its further application for the improvement of speaker diarization performance. We propose the use of three spatial cross-correlationbased parameters for overlap detection on distant microphone channel data. Spatial features from different microphone pairs are fused by means of principal component analysis, linear discriminant analysis, or by a multi-layer perceptron. In addition, we also investigate the possibility of employing longterm prosodic information. The most suitable subset from a set of candidate prosodic features is determined in two steps. Firstly, a ranking according to mRMR criterion is obtained, and then, a standard hill-climbing wrapper approach is applied in order to determine the optimal number of features. The novel spatial as well as prosodic parameters are used in combination with spectral-based features suggested previously in the literature. In experiments conducted on AMI meeting data, we show that the newly proposed features do contribute to the detection of overlapping speech, especially on data originating from a single recording site. In speaker diarization, for segments including detected speaker overlap, a second speaker label is picked, and such segments are also discarded from the model training. The proposed overlap labeling technique is integrated in Viterbi decoding, a part of the diarization algorithm. During the system development it was discovered that it is favorable to do an independent optimization of overlap exclusion and labeling with respect to the overlap detection system. We report improvements over the baseline diarization system on both single- and multi-site AMI data. Preliminary experiments with NIST RT data show DER improvement on the RT ¿09 meeting recordings as well. The addition of beamforming and TDOA feature stream into the baseline diarization system, which was aimed at improving the clustering process, results in a bit higher effectiveness of the overlap labeling algorithm. A more detailed analysis on the overlap exclusion behavior reveals big improvement contrasts between individual meeting recordings as well as between various settings of the overlap detection operation point. However, a high performance variability across different recordings is also typical of the baseline diarization system, without any overlap handling

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Speaker Diarization

Disertační práce se zaměřuje na téma diarizace řečníků, což je úloha zpracování řeči typicky charakterizovaná otázkou "Kdo kdy mluví?". Práce se také zabývá související úlohou detekce překrývající se řeči, která je velmi relevantní pro diarizaci. Teoretická část práce poskytuje přehled existujících metod diarizace řečníků, a to jak těch offline, tak online, a přibližuje několik problematických oblastí, které byly identifikovány v rané fázi autorčina výzkumu. V práci je také předloženo rozsáhlé srovnání existujících systémů se zaměřením na jejich uváděné výsledky. Jedna kapitola se také zaměřuje na téma překrývající se řeči a na metody její detekce. Experimentální část práce předkládá praktické výstupy, kterých bylo dosaženo. Experimenty s diarizací se zaměřovaly zejména na online systém založený na GMM a na i-vektorový systém, který měl offline i online varianty. Závěrečná sekce experimentů také přibližuje nově navrženou metodu pro detekci překrývající se řeči, která je založena na konvoluční neuronové síti.ObhájenoThe thesis focuses on the topic of speaker diarization, a speech processing task that is commonly characterized as the question "Who speaks when?". It also addresses the related task of overlapping speech detection, which is very relevant for diarization. The theoretical part of the thesis provides an overview of existing diarization approaches, both offline and online, and discusses some of the problematic areas which were identified in early stages of the author's research. The thesis also includes an extensive comparison of existing diarization systems, with focus on their reported performance. One chapter is also dedicated to the topic of overlapping speech and the methods of its detection. The experimental part of the thesis then presents the work which has been done on speaker diarization, which was focused mostly on a GMM-based online diarization system and an i-vector based system with both offline and online variants. The final section also details a newly proposed approach for detecting overlapping speech using a convolutional neural network

Automatic Emotion Recognition: Quantifying Dynamics and Structure in Human Behavior.

Emotion is a central part of human interaction, one that has a huge influence on its overall tone and outcome. Today's human-centered interactive technology can greatly benefit from automatic emotion recognition, as the extracted affective information can be used to measure, transmit, and respond to user needs. However, developing such systems is challenging due to the complexity of emotional expressions and their dynamics in terms of the inherent multimodality between audio and visual expressions, as well as the mixed factors of modulation that arise when a person speaks. To overcome these challenges, this thesis presents data-driven approaches that can quantify the underlying dynamics in audio-visual affective behavior. The first set of studies lay the foundation and central motivation of this thesis. We discover that it is crucial to model complex non-linear interactions between audio and visual emotion expressions, and that dynamic emotion patterns can be used in emotion recognition. Next, the understanding of the complex characteristics of emotion from the first set of studies leads us to examine multiple sources of modulation in audio-visual affective behavior. Specifically, we focus on how speech modulates facial displays of emotion. We develop a framework that uses speech signals which alter the temporal dynamics of individual facial regions to temporally segment and classify facial displays of emotion. Finally, we present methods to discover regions of emotionally salient events in a given audio-visual data. We demonstrate that different modalities, such as the upper face, lower face, and speech, express emotion with different timings and time scales, varying for each emotion type. We further extend this idea into another aspect of human behavior: human action events in videos. We show how transition patterns between events can be used for automatically segmenting and classifying action events. Our experimental results on audio-visual datasets show that the proposed systems not only improve performance, but also provide descriptions of how affective behaviors change over time. We conclude this dissertation with the future directions that will innovate three main research topics: machine adaptation for personalized technology, human-human interaction assistant systems, and human-centered multimedia content analysis.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133459/1/yelinkim_1.pd

VenoMave: Targeted Poisoning Against Speech Recognition

The wide adoption of Automatic Speech Recognition (ASR) remarkably enhanced human-machine interaction. Prior research has demonstrated that modern ASR systems are susceptible to adversarial examples, i.e., malicious audio inputs that lead to misclassification by the victim's model at run time. The research question of whether ASR systems are also vulnerable to data-poisoning attacks is still unanswered. In such an attack, a manipulation happens during the training phase: an adversary injects malicious inputs into the training set to compromise the neural network's integrity and performance. Prior work in the image domain demonstrated several types of data-poisoning attacks, but these results cannot directly be applied to the audio domain. In this paper, we present the first data-poisoning attack against ASR, called VenoMave. We evaluate our attack on an ASR system that detects sequences of digits. When poisoning only 0.17% of the dataset on average, we achieve an attack success rate of 86.67%. To demonstrate the practical feasibility of our attack, we also evaluate if the target audio waveform can be played over the air via simulated room transmissions. In this more realistic threat model, VenoMave still maintains a success rate up to 73.33%. We further extend our evaluation to the Speech Commands corpus and demonstrate the scalability of VenoMave to a larger vocabulary. During a transcription test with human listeners, we verify that more than 85% of the original text of poisons can be correctly transcribed. We conclude that data-poisoning attacks against ASR represent a real threat, and we are able to perform poisoning for arbitrary target input files while the crafted poison samples remain inconspicuous