Using auxiliary sources of knowledge for automatic speech recognition

Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems usually use cepstral features as acoustic observation and phonemes as subword units. Speech signal exhibits wide range of variability such as, due to environmental variation, speaker variation. This leads to different kinds of mismatch, such as, mismatch between acoustic features and acoustic models or mismatch between acoustic features and pronunciation models (given the acoustic models). The main focus of this work is on integrating auxiliary knowledge sources into standard ASR systems so as to make the acoustic models more robust to the variabilities in the speech signal. We refer to the sources of knowledge that are able to provide additional information about the sources of variability as auxiliary sources of knowledge. The auxiliary knowledge sources that have been primarily investigated in the present work are auxiliary features and auxiliary subword units. Auxiliary features are secondary source of information that are outside of the standard cepstral features. They can be estimation from the speech signal (e.g., pitch frequency, short-term energy and rate-of-speech), or additional measurements (e.g., articulator positions or visual information). They are correlated to the standard acoustic features, and thus can aid in estimating better acoustic models, which would be more robust to variabilities present in the speech signal. The auxiliary features that have been investigated are pitch frequency, short-term energy and rate-of-speech. These features can be modelled in standard ASR either by concatenating them to the standard acoustic feature vectors or by using them to condition the emission distribution (as done in gender-based acoustic modelling). We have studied these two approaches within the framework of hybrid HMM/artificial neural networks based ASR, dynamic Bayesian network based ASR and TANDEM system on different ASR tasks. Our studies show that by modelling auxiliary features along with standard acoustic features the performance of the ASR system can be improved in both clean and noisy conditions. We have also proposed an approach to evaluate the adequacy of the baseform pronunciation model of words. This approach allows us to compare between different acoustic models as well as to extract pronunciation variants. Through the proposed approach to evaluate baseform pronunciation model, we show that the matching and discriminative properties of single baseform pronunciation can be improved by integrating auxiliary knowledge sources in standard ASR. Standard ASR systems use usually phonemes as the subword units in a Markov chain to model words. In the present thesis, we also study a system where word models are described by two parallel chains of subword units: one for phonemes and the other are for graphemes (phoneme-grapheme based ASR). Models for both types of subword units are jointly learned using maximum likelihood training. During recognition, decoding is performed using either or both of the subword unit chains. In doing so, we thus have used graphemes as auxiliary subword units. The main advantage of using graphemes is that the word models can be defined easily using the orthographic transcription, thus being relatively noise free as compared to word models based upon phoneme units. At the same time, there are drawbacks to using graphemes as subword units, since there is a weak correspondence between the grapheme and the phoneme in languages such as English. Experimental studies conducted for American English on different ASR tasks have shown that the proposed phoneme-grapheme based ASR system can perform better than the standard ASR system that uses only phonemes as its subword units. Furthermore, while modelling context-dependent graphemes (similar to context-dependent phonemes), we observed that context-dependent graphemes behave like phonemes. ASR studies conducted on different tasks showed that by modelling context-dependent graphemes only (without any phonetic information) performance competitive to the state-of-the-art context-dependent phoneme-based ASR system can be obtained.

Trustworthy Biometric Verification under Spoofing Attacks

Ivana Chingovska

The need for automation of the identity recognition process for a vast number of applications resulted in great advancement of biometric systems in the recent years. Yet, many studies indicate that these systems suffer from vulnerabilities to spoofing (presentation) attacks: a weakness that may compromise their usage in many cases. Face verification systems account for one of the most attractive spoofing targets, due to the easy access to face images of users, as well as the simplicity of the spoofing attack manufacturing process. Many counter-measures to spoofing have been proposed in the literature. They are based on different cues that are used to distinguish between real accesses and spoofing attacks. The task of detecting spoofing attacks is most often considered as a binary classification problem, with real accesses being the positive class and spoofing attacks being the negative class. The main objective of this thesis is to put the problem of anti-spoofing in a wider context, with an accent on its cooperation with a biometric verification system. In such a context, it is important to adopt an integrated perspective on biometric verification and anti-spoofing. In this thesis we identify and address three points where integration of the two systems is of interest. The first integration point is situated at input-level. At this point, we are concerned with providing a unified information that both verification and anti-spoofing systems use. The unified information includes the samples used to enroll clients in the system, as well as the identity claims of the client at query time. We design two anti-spoofing schemes, one with a generative and one with a discriminative approach, which we refer to as client-specific, as opposed to the traditional client-independent ones. The proposed methods are applied on several case studies for the face mode. Overall, the experimental results prove the integration to be beneficial for creating trustworthy face verification systems. At input-level, the results show the advantage of the client-specific approaches over the client-independent ones. At output-level, they present a comparison of the fusion methods. The case studies are furthermore used to demonstrate the EPS framework and its potential in evaluation of biometric verification systems under spoofing attacks. The source code for the full set of methods is available as free software, as a satellite package to the free signal processing and machine learning toolbox Bob. It can be used to reproduce the results of the face mode case studies presented in this thesis, as well as to perform additional analysis and improve the proposed methods. Furthermore, it can be used to design case studies applying the proposed methods to other biometric modes. At the second integration point, situated at output-level, we address the issue of combining the output of biometric verification and anti-spoofing systems in order to achieve an optimal combined decision about an input sample. We adopt a multiple expert fusion approach and we investigate several fusion methods, comparing the verification performance and robustness to spoofing of the fused systems. The third integration point is associated with the evaluation process. The integrated perspective implies three types of inputs for the biometric system: real accesses, zero-effort impostors and spoofing attacks. We propose an evaluation methodology for biometric verification systems under spoofing attacks, called Expected Performance and Spoofability (EPS) framework, which accounts for all the three types of input and the error rates associated with them. Within this framework, we propose the EPS Curve (EPSC), which enables unbiased comparison of systems.

EPFL2016

Error handling in multimodal voice-enabled interfaces of tour-guide robots using graphical models

Plamen Prodanov

Mobile service robots are going to play an increasing role in the society of humans. Voice-enabled interaction with service robots becomes very important, if such robots are to be deployed in real-world environments and accepted by the vast majority of potential human users. The research presented in this thesis addresses the problem of speech recognition integration in an interactive voice-enabled interface of a service robot, in particular a tour-guide robot. The task of a tour-guide robot is to engage visitors to mass exhibitions (users) in dialogue providing the services it is designed for (e.g. exhibit presentations) within a limited time. In managing tour-guide dialogues, extracting the user goal (intention) for requesting a particular service at each dialogue state is the key issue. In mass exhibition conditions speech recognition errors are inevitable because of noisy speech and uncooperative users of robots with no prior experience in robotics. They can jeopardize the user goal identification. Wrongly identified user goals can lead to communication failures. Therefore, to reduce the risk of such failures, methods for detecting and compensating for communication failures in human-robot dialogue are needed. During the short-term interaction with visitors, the interpretation of the user goal at each dialogue state can be improved by combining speech recognition in the speech modality with information from other available robot modalities. The methods presented in this thesis exploit probabilistic models for fusing information from speech and auxiliary modalities of the robot for user goal identification and communication failure detection. To compensate for the detected communication failures we investigate multimodal methods for recovery from communication failures. To model the process of modality fusion, taking into account the uncertainties in the information extracted from each input modality during human-robot interaction, we use the probabilistic framework of Bayesian networks. Bayesian networks are graphical models that represent a joint probability function over a set of random variables. They are used to model the dependencies among variables associated with the user goals, modality related events (e.g. the event of user presence that is inferred from the laser scanner modality of the robot), and observed modality features providing evidence in favor of these modality events. Bayesian networks are used to calculate posterior probabilities over the possible user goals at each dialogue state. These probabilities serve as a base in deciding if the user goal is valid, i.e. if it can be mapped into a tour-guide service (e.g. exhibit presentation) or is undefined – signaling a possible communication failure. The Bayesian network can be also used to elicit probabilities over the modality events revealing information about the possible cause for a communication failure. Introducing new user goal aspects (e.g. new modality events and related features) that provide auxiliary information for detecting communication failures makes the design process cumbersome, calling for a systematic approach in the Bayesian network modelling. Generally, introducing new variables for user goal identification in the Bayesian networks can lead to complex and computationally expensive models. In order to make the design process more systematic and modular, we adapt principles from the theory of grounding in human communication. When people communicate, they resolve understanding problems in a collaborative joint effort of providing evidence of common shared knowledge (grounding). We use Bayesian network topologies, tailored to limited computational resources, to model a state-based grounding model fusing information from three different input modalities (laser, video and speech) to infer possible grounding states. These grounding states are associated with modality events showing if the user is present in range for communication, if the user is attending to the interaction, whether the speech modality is reliable, and if the user goal is valid. The state-based grounding model is used to compute probabilities that intermediary grounding states have been reached. This serves as a base for detecting if the the user has reached the final grounding state, or wether a repair dialogue sequence is needed. In the case of a repair dialogue sequence, the tour-guide robot can exploit the multiple available modalities along with speech. For example, if the user has failed to reach the grounding state related to her/his presence in range for communication, the robot can use its move modality to search and attract the attention of the visitors. In the case when speech recognition is detected to be unreliable, the robot can offer the alternative use of the buttons modality in the repair sequence. Given the probability of each grounding state, and the dialogue sequence that can be executed in the next dialogue state, a tour-guide robot has different preferences on the possible dialogue continuation. If the possible dialogue sequences at each dialogue state are defined as actions, the introduced principle of maximum expected utility (MEU) provides an explicit way of action selection, based on the action utility, given the evidence about the user goal at each dialogue state. Decision networks, constructed as graphical models based on Bayesian networks are proposed to perform MEU-based decisions, incorporating the utility of the actions to be chosen at each dialogue state by the tour-guide robot. These action utilities are defined taking into account the tour-guide task requirements. The proposed graphical models for user goal identification and dialogue error handling in human-robot dialogue are evaluated in experiments with multimodal data. These data were collected during the operation of the tour-guide robot RoboX at the Autonomous System Lab of EPFL and at the Swiss National Exhibition in 2002 (Expo.02). The evaluation experiments use component and system level metrics for technical (objective) and user-based (subjective) evaluation. On the component level, the technical evaluation is done by calculating accuracies, as objective measures of the performance of the grounding model, and the resulting performance of the user goal identification in dialogue. The benefit of the proposed error handling framework is demonstrated comparing the accuracy of a baseline interactive system, employing only speech recognition for user goal identification, and a system equipped with multimodal grounding models for error handling.

EPFL2006

Automatic social role recognition and its application in structuring multiparty interactions

Ashtosh Sapru

Automatic processing of multiparty interactions is a research domain with important applications in content browsing, summarization and information retrieval. In recent years, several works have been devoted to find regular patterns which speakers exhibit in a multiparty interaction also known as social roles. Most of the research in literature has generally focused on recognition of scenario specific formal roles. More recently, role coding schemes based on informal social roles have been proposed in literature, defining roles based on the behavior speakers have in the functioning of a small group interaction. Informal social roles represent a flexible classification scheme that can generalize across different scenarios of multiparty interaction. In this thesis, we focus on automatic recognition of informal social roles and exploit the influence of informal social roles on speaker behavior for structuring multiparty interactions. To model speaker behavior, we systematically explore various verbal and non verbal cues extracted from turn taking patterns, vocal expression and linguistic style. The influence of social roles on the behavior cues exhibited by a speaker is modeled using a discriminative approach based on conditional random fields. Experiments performed on several hours of meeting data reveal that classification using conditional random fields improves the role recognition performance. We demonstrate the effectiveness of our approach by evaluating it on previously unseen scenarios of multiparty interaction. Furthermore, we also consider whether formal roles and informal roles can be automatically predicted by the same verbal and nonverbal features. We exploit the influence of social roles on turn taking patterns to improve speaker diarization under distant microphone condition. Our work extends the Hidden Markov model (HMM)- Gaussian mixture model (GMM) speaker diarization system, and is based on jointly estimating both the speaker segmentation and social roles in an audio recording. We modify the minimum duration constraint in HMM-GMM diarization system by using role information to model the expected duration of speaker's turn. We also use social role n-grams as prior information to model speaker interaction patterns. Finally, we demonstrate the application of social roles for the problem of topic segmentation in meetings. We exploit our findings that social roles can dynamically change in conversations and use this information to predict topic changes in meetings. We also present an unsupervised method for topic segmentation which combines social roles and lexical cohesion. Experimental results show that social roles improve performance of both speaker diarization and topic segmentation.

EPFL2015