Cross-Lingual Voice Conversion with Non-Parallel Data

In this project a Phonetic Posteriorgram (PPG) based Voice Conversion system is implemented. The main goal is to perform and evaluate conversions of singing voice. The cross-gender and cross-lingual scenarios are considered. Additionally, the use of spectral envelope based MFCC and pseudo-singing dataset for ASR training are proposed in order to improve the performance of the system in the singing context

Developing Sparse Representations for Anchor-Based Voice Conversion

Voice conversion is the task of transforming speech from one speaker to sound as if it was produced by another speaker, changing the identity while retaining the linguistic content. There are many methods for performing voice conversion, but oftentimes these methods have onerous training requirements or fail in instances where one speaker has a nonnative accent. To address these issues, this dissertation presents and evaluates a novel “anchor-based” representation of speech that separates speaker content from speaker identity by modeling how speakers form English phonemes. We call the proposed method Sparse, Anchor-Based Representation of Speech (SABR), and explore methods for optimizing the parameters of this model in native-to-native and native-to-nonnative voice conversion contexts. We begin the dissertation by demonstrating how sparse coding in combination with a compact, phoneme-based dictionary can be used to separate speaker identity from content in objective and subjective tests. The formulation of the representation then presents several research questions. First, we propose a method for improving the synthesis quality by using the sparse coding residual in combination with a frequency warping algorithm to convert the residual from the source to target speaker’s space, and add it to the target speaker’s estimated spectrum. Experimentally, we find that synthesis quality is significantly improved via this transform. Second, we propose and evaluate two methods for selecting and optimizing SABR anchors in native-to-native and native-to-nonnative voice conversion. We find that synthesis quality is significantly improved by the proposed methods, especially in native-to- nonnative voice conversion over baseline algorithms. In a detailed analysis of the algorithms, we find they focus on phonemes that are difficult for nonnative speakers of English or naturally have multiple acoustic states. Following this, we examine methods for adding in temporal constraints to SABR via the Fused Lasso. The proposed method significantly reduces the inter-frame variance in the sparse codes over other methods that incorporate temporal features into sparse coding representations. Finally, in a case study, we examine the use of the SABR methods and optimizations in the context of a computer aided pronunciation training system for building “Golden Speakers”, or ideal models for nonnative speakers of a second language to learn correct pronunciation. Under the hypothesis that the optimal “Golden Speaker” was the learner’s voice, synthesized with a native accent, we used SABR to build voice models for nonnative speakers and evaluated the resulting synthesis in terms of quality, identity, and accentedness. We found that even when deployed in the field, the SABR method generated synthesis with low accentedness and similar acoustic identity to the target speaker, validating the use of the method for building “golden speakers”

Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques

The growing use of voice user interfaces has led to a surge in the collection and storage of speech data. While data collection allows for the development of efficient tools powering most speech services, it also poses serious privacy issues for users as centralized storage makes private personal speech data vulnerable to cyber threats. With the increasing use of voice-based digital assistants like Amazon's Alexa, Google's Home, and Apple's Siri, and with the increasing ease with which personal speech data can be collected, the risk of malicious use of voice-cloning and speaker/gender/pathological/etc. recognition has increased. This thesis proposes solutions for anonymizing speech and evaluating the degree of the anonymization. In this work, anonymization refers to making personal speech data unlinkable to an identity while maintaining the usefulness (utility) of the speech signal (e.g., access to linguistic content). We start by identifying several challenges that evaluation protocols need to consider to evaluate the degree of privacy protection properly. We clarify how anonymization systems must be configured for evaluation purposes and highlight that many practical deployment configurations do not permit privacy evaluation. Furthermore, we study and examine the most common voice conversion-based anonymization system and identify its weak points before suggesting new methods to overcome some limitations. We isolate all components of the anonymization system to evaluate the degree of speaker PPI associated with each of them. Then, we propose several transformation methods for each component to reduce as much as possible speaker PPI while maintaining utility. We promote anonymization algorithms based on quantization-based transformation as an alternative to the most-used and well-known noise-based approach. Finally, we endeavor a new attack method to invert anonymization.Comment: PhD Thesis Pierre Champion | Universit\'e de Lorraine - INRIA Nancy | for associated source code, see https://github.com/deep-privacy/SA-toolki

Attention-Inspired Artificial Neural Networks for Speech Processing: A Systematic Review

Artificial Neural Networks (ANNs) were created inspired by the neural networks in the human brain and have been widely applied in speech processing. The application areas of ANN include: Speech recognition, speech emotion recognition, language identification, speech enhancement, and speech separation, amongst others. Likewise, given that speech processing performed by humans involves complex cognitive processes known as auditory attention, there has been a growing amount of papers proposing ANNs supported by deep learning algorithms in conjunction with some mechanism to achieve symmetry with the human attention process. However, while these ANN approaches include attention, there is no categorization of attention integrated into the deep learning algorithms and their relation with human auditory attention. Therefore, we consider it necessary to have a review of the different ANN approaches inspired in attention to show both academic and industry experts the available models for a wide variety of applications. Based on the PRISMA methodology, we present a systematic review of the literature published since 2000, in which deep learning algorithms are applied to diverse problems related to speech processing. In this paper 133 research works are selected and the following aspects are described: (i) Most relevant features, (ii) ways in which attention has been implemented, (iii) their hypothetical relationship with human attention, and (iv) the evaluation metrics used. Additionally, the four publications most related with human attention were analyzed and their strengths and weaknesses were determined

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli