Personality prediction based on intonation stylization

This study's aim is to predict speaker personality from intonation patterns in spoken dialogs. Intonation patterns were extracted by a parametric superpositional stylization approach that allows for pattern description on a parametric as well as on a categorical level. Based on features derived from these representations we trained support vector machines and fitted generalized linear regression models to predict speaker personality with respect to the four dimensions acting, extroversion, other-directedness, and sensitivity. The personality classification accuracies ranged from 79 to 91%

Prosodic detail in Neapolitan Italian

Recent findings on phonetic detail have been taken as supporting exemplar-based approaches to prosody. Through four experiments on both production and perception of both melodic and temporal detail in Neapolitan Italian, we show that prosodic detail is not incompatible with abstractionist approaches either. Specifically, we suggest that the exploration of prosodic detail leads to a refined understanding of the relationships between the richly specified and continuous varying phonetic information on one side, and coarse phonologically structured contrasts on the other, thus offering insights on how pragmatic information is conveyed by prosody

Prosodic detail in Neapolitan Italian

Recent findings on phonetic detail have been taken as supporting exemplar-based approaches to prosody. Through four experiments on both production and perception of both melodic and temporal detail in Neapolitan Italian, we show that prosodic detail is not incompatible with abstractionist approaches either. Specifically, we suggest that the exploration of prosodic detail leads to a refined understanding of the relationships between the richly specified and continuous varying phonetic information on one side, and coarse phonologically structured contrasts on the other, thus offering insights on how pragmatic information is conveyed by prosody

Prosodic detail in Neapolitan Italian

Recent findings on phonetic detail have been taken as supporting exemplar-based approaches to prosody. Through four experiments on both production and perception of both melodic and temporal detail in Neapolitan Italian, we show that prosodic detail is not incompatible with abstractionist approaches either. Specifically, we suggest that the exploration of prosodic detail leads to a refined understanding of the relationships between the richly specified and continuous varying phonetic information on one side, and coarse phonologically structured contrasts on the other, thus offering insights on how pragmatic information is conveyed by prosody

Datenbasierte und linguistisch interpretierbare Intonationsmodellierung

In this thesis a data-driven and linguistically interpretable intonation model for the automatic analysis and synthesis of fundamental frequency (F0) contours was developed. The model can be characterised as parametric, contour-based, and superpositional. Its intonation representation consists of a superposition of global and local contour classes and can be derived in a purely data-driven manner, which guarantees consistency and easy adaptability to new data. The model's linguistic interpretability was examined by automatic linguistic corpus analyses resulting in hypotheses about possible relations between contours and linguistic concepts. These hypotheses were subsequently tested by perception experiments. By these means a systematic linguistic anchoring of the model was achieved in form of a decision tree to predict the linguistically appropriate contour class. The adequacy of its predictions was assured by a further perception test. Due to its simultaneous signal proximity and linguistic anchoring, the model covers the entire chain from text to signal and therefore can be used for intonation analysis and generation on a linguistic as well as on a phonetic-acoustic level. It is qualified for employment in speech technology applications as well as in phonetic fundamental research to automatically analyse raw speech data

Computational Approaches to the Syntax–Prosody Interface: Using Prosody to Improve Parsing

Prosody has strong ties with syntax, since prosody can be used to resolve some syntactic ambiguities. Syntactic ambiguities have been shown to negatively impact automatic syntactic parsing, hence there is reason to believe that prosodic information can help improve parsing. This dissertation considers a number of approaches that aim to computationally examine the relationship between prosody and syntax of natural languages, while also addressing the role of syntactic phrase length, with the ultimate goal of using prosody to improve parsing. Chapter 2 examines the effect of syntactic phrase length on prosody in double center embedded sentences in French. Data collected in a previous study were reanalyzed using native speaker judgment and automatic methods (forced alignment). Results demonstrate similar prosodic splitting behavior as in English in contradiction to the original study’s findings. Chapter 3 presents a number of studies examining whether syntactic ambiguity can yield different prosodic patterns, allowing humans and/or computers to resolve the ambiguity. In an experimental study, humans disambiguated sentences with prepositional phrase- (PP)-attachment ambiguity with 49% accuracy presented as text, and 63% presented as audio. Machine learning on the same data yielded an accuracy of 63-73%. A corpus study on the Switchboard corpus used both prosodic breaks and phrase lengths to predict the attachment, with an accuracy of 63.5% for PP-attachment sentences, and 71.2% for relative clause attachment. Chapter 4 aims to identify aspects of syntax that relate to prosody and use these in combination with prosodic cues to improve parsing. The aspects identified (dependency configurations) are based on dependency structure, reflecting the relative head location of two consecutive words, and are used as syntactic features in an ensemble system based on Recurrent Neural Networks, to score parse hypotheses and select the most likely parse for a given sentence. Using syntactic features alone, the system achieved an improvement of 1.1% absolute in Unlabelled Attachment Score (UAS) on the test set, above the best parser in the ensemble, while using syntactic features combined with prosodic features (pauses and normalized duration) led to a further improvement of 0.4% absolute. The results achieved demonstrate the relationship between syntax, syntactic phrase length, and prosody, and indicate the ability and future potential of prosody to resolve ambiguity and improve parsing

Parametric Modeling of Intonation Using Vector Quantization

In this study we propose a data-based approach to intonation modeling using vector quantization. The model is based on an F0 parametrization with an especially designed approximation function. The parameter vectors found are vector quantized with varying codebook sizes. This method is motivated by intonation theories that suggest that pitch accent and boundary phenomena can be described by a distinct number of different types. We use classification trees to predict the F0 movements represented in the codebook from a set of features. We assessed the quality of the model by numerical measures and perceptual testing. The tests show that our method performs well when compared with other methods of intonation modeling