Prosodic Representations of Prominence Classification Neural Networks and Autoencoders Using Bottleneck Features

Prominence perception has been known to correlate with a complex interplay of the acoustic features of energy, fundamental frequency, spectral tilt, and duration. The contribution and importance of each of these features in distinguishing between prominent and non-prominent units in speech is not always easy to determine, and more so, the prosodic representations that humans and automatic classifiers learn have been difficult to interpret. This work focuses on examining the acoustic prosodic representations that binary prominence classification neural networks and autoencoders learn for prominence. We investigate the complex features learned at different layers of the network as well as the 10-dimensional bottleneck features (BNFs), for the standard acoustic prosodic correlates of prominence separately and in combination. We analyze and visualize the BNFs obtained from the prominence classification neural networks as well as their network activations. The experiments are conducted on a corpus of Dutch continuous speech with manually annotated prominence labels. Our results show that the prosodic representations obtained from the BNFs and higher-dimensional non-BNFs provide good separation of the two prominence categories, with, however, different partitioning of the BNF space for the distinct features, and the best overall separation obtained for F0.Peer reviewe

Prosodic Prominence and Boundaries in Sequence-to-Sequence Speech Synthesis

Recent advances in deep learning methods have elevated synthetic speech quality to human level, and the field is now moving towards addressing prosodic variation in synthetic speech.Despite successes in this effort, the state-of-the-art systems fall short of faithfully reproducing local prosodic events that give rise to, e.g., word-level emphasis and phrasal structure. This type of prosodic variation often reflects long-distance semantic relationships that are not accessible for end-to-end systems with a single sentence as their synthesis domain. One of the possible solutions might be conditioning the synthesized speech by explicit prosodic labels, potentially generated using longer portions of text. In this work we evaluate whether augmenting the textual input with such prosodic labels capturing word-level prominence and phrasal boundary strength can result in more accurate realization of sentence prosody. We use an automatic wavelet-based technique to extract such labels from speech material, and use them as an input to a tacotron-like synthesis system alongside textual information. The results of objective evaluation of synthesized speech show that using the prosodic labels significantly improves the output in terms of faithfulness of f0 and energy contours, in comparison with state-of-the-art implementations.Peer reviewe

Comparative analysis of majority language influence on North Sámi prosody using WaveNet-based modeling

The Finnmark North Sami is a variety of North Sami language, an indigenous, endangered minority language spoken in the northernmost parts of Norway and Finland. The speakers of this language are bilingual, and regularly speak the majority language (Finnish or Norwegian) as well as their own North Sami variety. In this paper we investigate possible influences of these majority languages on prosodic characteristics of Finnmark North Sami, and associate them with prosodic patterns prevalent in the majority languages. We present a novel methodology that: (a) automatically finds the portions of speech (words) where the prosodic differences based on majority languages are most robustly manifested; and (b) analyzes the nature of these differences in terms of intonational patterns. For the first step, we trained convolutional WaveNet speech synthesis models on North Sami speech material, modified to contain purely prosodic information, and used conditioning embeddings to find words with the greatest differences between the varieties. The subsequent exploratory analysis suggests that the differences in intonational patterns between the two Finnmark North Sami varieties are not manifested uniformly across word types (based on part-of-speech category). Instead, we argue that the differences reflect phrase-level prosodic characteristics of the majority languages.Peer reviewe

Cross-linguistic Influences on Sentence Accent Detection in Background Noise.

This paper investigates whether sentence accent detection in a non-native language is dependent on (relative) similarity between prosodic cues to accent between the non-native and the native language, and whether cross-linguistic differences in the use of local and more widely distributed (i.e., non-local) cues to sentence accent detection lead to differential effects of the presence of background noise on sentence accent detection in a non-native language. We compared Dutch, Finnish, and French non-native listeners of English, whose cueing and use of prosodic prominence is gradually further removed from English, and compared their results on a phoneme monitoring task in different levels of noise and a quiet condition to those of native listeners. Overall phoneme detection performance was high for the native and the non-native listeners, but deteriorated to the same extent in the presence of background noise. Crucially, relative similarity between the prosodic cues to sentence accent of one's native language compared to that of a non-native language does not determine the ability to perceive and use sentence accent for speech perception in that non-native language. Moreover, proficiency in the non-native language is not a straightforward predictor of sentence accent perception performance, although high proficiency in a non-native language can seemingly overcome certain differences at the prosodic level between the native and non-native language. Instead, performance is determined by the extent to which listeners rely on local cues (English and Dutch) versus cues that are more distributed (Finnish and French), as more distributed cues survive the presence of background noise better

Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations

In this paper we introduce a new natural language processing dataset and benchmark for predicting prosodic prominence from written text. To our knowledge this will be the largest publicly available dataset with prosodic labels. We describe the dataset construction and the resulting benchmark dataset in detail and train a number of different models ranging from feature-based classifiers to neural network systems for the prediction of discretized prosodic prominence. We show that pre-trained contextualized word representations from BERT outperform the other models even with less than 10% of the training data. Finally we discuss the dataset in light of the results and point to future research and plans for further improving both the dataset and methods of predicting prosodic prominence from text. The dataset and the code for the models are publicly available.Peer reviewe

The Effects of a Digital Articulatory Game on the Ability to Perceive Speech-Sound Contrasts in Another Language

Digital and mobile devices enable easy access to applications for the learning of foreign languages. However, experimental studies on the effectiveness of these applications are scarce. Moreover, it is not understood whether the effects of speech and language training generalize to features that are not trained. To this end, we conducted a four-week intervention that focused on articulatory training and learning of English words in 6-7-year-old Finnish-speaking children who used a digital language-learning game app Pop2talk. An essential part of the app is automatic speech recognition that enables assessing children's utterances and giving instant feedback to the players. The generalization of the effects of such training in English were explored by using discrimination tasks before and after training (or the same period of time in a control group). The stimuli of the discrimination tasks represented phonetic contrasts from two non-trained languages, including Russian sibilant consonants and Mandarin tones. We found some improvement with the Russian sibilant contrast in the gamers but it was not statistically significant. No improvement was observed for the tone contrast for the gaming group. A control group with no training showed no improvement in either contrast. The pattern of results suggests that the game may have improved the perception of non-trained speech sounds in some but not all individuals, yet the effects of motivation and attention span on their performance could not be excluded with the current methods. Children's perceptual skills were linked to their word learning in the control group but not in the gaming group where recurrent exposure enabled learning also for children with poorer perceptual skills. Together, the results demonstrate beneficial effects of learning via a digital application, yet raise a need for further research of individual differences in learning.Peer reviewe

Cognitive and probabilistic basis of prominence perception in speech

The research in this thesis examines the topic of the cognitive and probabilistic nature of prominence perception in speech. In recent years, there has been an accumulating number of studies from linguistics, phonetics, and neuroscience providing evidence that (i) prominence is related to attention- and expectation-based factors, (ii) frequency and predictability effects hold an important role in language processing, accounting for several linguistic phenomena, and (iii) the human brain represents information in a probabilistic way, with humans behaving as optimal probabilistic observers. On the basis of this evidence, the relationship between prominence, attention, and predictability is explored. A hypothesis is proposed suggesting that prominence perception in speech is connected with the unpredictability of prosodic features that draw the listeners' attention to the surprising aspects of the input. This thesis consists of a series of computational and behavioral studies that investigate different aspects of the prominence–attention–predictability tripartite. The core idea throughout this work is to investigate the probabilistic relations that take place at the acoustic prosodic domain through statistical modeling of the acoustic correlates of prominence, examining their relationship with the concurrent prominent/non-prominent units. As the probabilistic view of prominence also implies that listeners utilize some type of statistical learning mechanism operating at the suprasegmental acoustic prosodic level, a number of behavioral experiments are also conducted. The aim of these experiments is to understand whether human listeners are sensitive to the statistical regularities of suprasegmental speech acoustics and, if so, to what extent. A basic application of statistical models for the automatic detection of prominence in speech is also reported. As a result of these studies, the thesis shows that predictability at the acoustic prosodic level is strongly correlated with human listeners' perception of prominence in speech. This statistical connection, however, is not fixed but depends on the listeners' experience with the language and thereby with subjective expectations of prosodic outcomes. This is illuminated by results that show that the human perceptual system appears to quickly adapt to the suprasegmental probabilistic structure of the incoming speech, causing the prosodic patterns that are less frequent in the recent discourse-specific acoustics to be more prominent. Thus, the experiments indicate a type of statistical learning mechanism operating at the suprasegmental acoustic level. Finally, a practical application of the predictability framework to the unsupervised detection of prominence in speech is described. Experiments in several languages show that the method provides high agreement with human judgments of prominence despite not having access to prominence labeling during training of the detector