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1. Characteristics of Text-to-Speech and Other Corpora

   https://doi.org/DOI: 10.21437/SpeechProsody.2018-140  

   Erica Cooper, Emily Li ( June 2019 , Speech Prosocy 2018)

   Extensive TTS corpora exist for commercial systems created for high-resource languages such as Mandarin, English, and Japanese. Speakers recorded for these corpora are typically instructed to maintain constant f0, energy, and speaking rate and are recorded in ideal acoustic environments, producing clean, consistent audio. We have been developing TTS systems from ""found"" data collected for other purposes (e.g. training ASR systems) or available on the web (e.g. news broadcasts, audiobooks) to produce TTS systems for low-resource languages (LRLs) which do not currently have expensive, commercial systems. This study investigates whether traditional TTS speakers do exhibit significantly less variation and better speaking characteristics than speakers in ""found"" genres. By examining characteristics of f0, energy, speaking rate, articulation, NHR, jitter, and shimmer in ""found” genres and comparing these to traditional TTS corpora, we have found that TTS recordings are indeed characterized by low mean pitch, standard deviation of energy, speaking rate, and level of articulation, and low mean and standard deviations of shimmer and NHR; in a number of respects these are quite similar to some ""found” genres. By identifying similarities and differences, we are able to identify objective methods for selecting ""found"" data to build TTS systems for LRLs. 

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2. Utterance Selection for Optimizing Intelligibility of TTS Voices Trained on ASR Data

   https://doi.org/DOI: 10.21437/Interspeech.2017-465  

   Erica Cooper, Xinyue Wang ( January 2017 , Interspeech 2017)

   This paper describes experiments in training HMM-based text-to-speech (TTS) voices on data collected for Automatic Speech Recognition (ASR) training. We compare a number of filtering techniques designed to identify the best utterances from a noisy, multi-speaker corpus for training voices, to exclude speech containing noise and to include speech close in nature to more traditionally-collected TTS corpora. We also evaluate the use of automatic speech recognizers for intelligibility assessment in comparison with crowdsourcing methods. While the goal of this work is to develop natural-sounding and intelligible TTS voices in Low Resource Languages (LRLs) rapidly and easily, without the expense of recording data specifically for this purpose, we focus on English initially to identify the best filtering techniques and evaluation methods. We find that, when a large amount of data is available, selecting from the corpus based on criteria such as standard deviation of f0, fast speaking rate, and hypo-articulation produces the most intelligible voices. 

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3. Subset Selection, Adaptation, Gemination and Prosody Prediction for Amharic Text-to-Speech Synthesis

   https://doi.org/10.21437/SSW.2019-37  

   Tesfaye Biru, Elshadai ; Tofik Mohammed, Yishak ; Tofu, David ; Cooper, Erica ; Hirschberg, Julia ( September 2019 , 10th ISCA Speech Synthesis Workshop)

   While large TTS corpora exist for commercial sys- tems created for high-resource languages such as Man- darin, English, and Spanish, for many languages such as Amharic, which are spoken by millions of people, this is not the case. We are working with “found” data collected for other purposes (e.g. training ASR systems) or avail- able on the web (e.g. news broadcasts, audiobooks) to produce TTS systems for low-resource languages which do not currently have expensive, commercial systems. This study describes TTS systems built for Amharic from “found” data and includes systems built from di erent acoustic-prosodic subsets of the data, systems built from combined high and lower quality data using adaptation, and systems which use prediction of Amharic gemination to improve naturalness as perceived by evaluators. 

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4. A Text-Analytic Method for Identifying Text Recycling in STEM Research Reports

   Anson, Ian G ; Moskovitz, Cary ; Anson, Chris M ( November 2019 , The journal of writing analytics)

   Background: Text recycling (hereafter TR)—the reuse of one’s own textual materials from one document in a new document—is a common but hotly debated and unsettled practice in many academic disciplines, especially in the context of peer-reviewed journal articles. Although several analytic systems have been used to determine replication of text—for example, for purposes of identifying plagiarism—they do not offer an optimal way to compare documents to determine the nature and extent of TR in order to study and theorize this as a practice in different disciplines. In this article, we first describe TR as a common phenomenon in academic publishing, then explore the challenges associated with trying to study the nature and extent of TR within STEM disciplines. We then describe in detail the complex processes we used to create a system for identifying TR across large corpora of texts, and the sentence-level string-distance lexical methods used to refine and test the system (White & Joy, 2004). The purpose of creating such a system is to identify legitimate cases of TR across large corpora of academic texts in different fields of study, allowing meaningful cross-disciplinary comparisons in future analyses of published work. The findings from such investigations will extend and refine our understanding of discourse practices in academic and scientific settings. Literature Review: Text-analytic methods have been widely developed and implemented to identify reused textual materials for detecting plagiarism, and there is considerable literature on such methods. (Instead of taking up space detailing this literature, we point readers to several recent reviews: Gupta, 2016; Hiremath & Otari, 2014; and Meuschke & Gipp, 2013). Such methods include fingerprinting, term occurrence analysis, citation analysis (identifying similarity in references and citations), and stylometry (statistically comparing authors’ writing styles; see Meuschke & Gipp, 2013). Although TR occurs in a wide range of situations, recent debate has focused on recycling from one published research paper to another—particularly in STEM fields (see, for example, Andreescu, 2013; Bouville, 2008; Bretag & Mahmud, 2009; Roig, 2008; Scanlon, 2007). An important step in better understanding the practice is seeing how authors actually recycle material in their published work. Standard methods for detecting plagiarism are not directly suitable for this task, as the objective is not to determine the presence or absence of reuse itself, but to study the types and patterns of reuse, including materials that are syntactically but not substantively distinct—such as “patchwriting” (Howard, 1999). In the present account of our efforts to create a text-analytic system for determining TR, we take a conventional alphabetic approach to text, in part because we did not aim at this stage of our project to analyze non-discursive text such as images or other media. However, although the project adheres to conventional definitions of text, with a focus on lexical replication, we also subscribe to context-sensitive approaches to text production. The results of applying the system to large corpora of published texts can potentially reveal varieties in the practice of TR as a function of different discourse communities and disciplines. Writers’ decisions within what appear to be canonical genres are contingent, based on adherence to or deviation from existing rules and procedures if and when these actually exist. Our goal is to create a system for analyzing TR in groups of texts produced by the same authors in order to determine the nature and extent of TR, especially across disciplinary areas, without judgment of scholars’ use of the practice. 

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5. Low tone bias during perception of period doubling

   https://doi.org/10.1121/10.0015927  

   Huang, Yaqian ( October 2022 , The Journal of the Acoustical Society of America)

   Period-doubled voice consists of two alternating periods with multiple frequencies and is often perceived as rough with an indeterminate pitch. Past pitch-matching studies in period-doubled voice found that the perceived pitch was lower as the degree of amplitude and frequency modulation between the two alternating periods increased. The perceptual outcome also differed across f0s and modulation types: a lower f0 prompted earlier identification of a lower pitch, and the matched pitch dropped more quickly in frequency- than amplitude-modulated tokens (Sun & Xu, 2002; Bergan & Titze, 2001). However, it is unclear how listeners perceive period doubling when identifying linguistic tones. In an artificial language learning paradigm, this study used resynthesized stimuli with alternating amplitudes and/or frequencies of varying degrees, based on a production study of period-doubled voice (Huang, 2022). Listeners were native speakers of English and Mandarin. We confirm the positive relationship between the modulation degree and the proportion of low tones heard, and find that frequency modulation biased listeners to choose more low-tone options than amplitude modulation. However, a higher f0 (300 Hz) leads to a low-tone percept in more amplitude-modulated tokens than a lower f0 (200 Hz). Both English and Mandarin listeners behaved similarly, suggesting that pitch perception during period doubling is not language-specific. Furthermore, period doubling is predicted to signal low tones in languages, even when the f0 is high. 

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