Speech interfaces are growing in popularity. Through a review of 68 research papers this work maps the trends, themes, findings and methods of empirical research on speech interfaces in HCI. We find that most studies are usability/theory-focused or explore wider system experiences, evaluating Wizard of Oz, prototypes, or developed systems by using self-report questionnaires to measure concepts like usability and user attitudes. A thematic analysis of the research found that speech HCI work focuses on nine key topics: system speech production, modality comparison, user speech production, assistive technology & accessibility, design insight, experiences with interactive voice response (IVR) systems, using speech technology for development, people's experiences with intelligent personal assistants (IPAs) and how user memory affects speech interface interaction. From these insights we identify gaps and challenges in speech research, notably the need to develop theories of speech interface interaction, grow critical mass in this domain, increase design work, and expand research from single to multiple user interaction contexts so as to reflect current use contexts. We also highlight the need to improve measure reliability, validity and consistency, in the wild deployment and reduce barriers to building fully functional speech interfaces for research. Author Keywords Speech interfaces; speech HCI; review; speech technology; voice user interfaces Research Highlights• Most papers focused on usability/theory-based or wider system experience research with a focus on Wizard of Oz and developed systems, though a lack of design work • Questionnaires on usability and user attitudes often used but few were reliable or validated • Thematic analysis showed nine primary research topics • Gaps in research critical mass, speech HCI theories, and multiple user contexts

This paper proposes an analysis method to separate the glottal source and vocal tract components of speech that is called Glottal Spectral Separation (GSS). This method can produce high-quality synthetic speech using an acoustic glottal source model. In the source-filter models commonly used in speech technology applications it is assumed the source is a spectrally flat excitation signal and the vocal tract filter can be represented by the spectral envelope of speech. Although this model can produce high-quality speech, it has limitations for voice transformation because it does not allow control over glottal parameters which are correlated with voice quality. The main problem with using a speech model that better represents the glottal source and the vocal tract filter is that current analysis methods for separating these components are not robust enough to produce the same speech quality as using a model based on the spectral envelope of speech. The proposed GSS method is an attempt to overcome this problem, and consists of the following three steps. Initially, the glottal source signal is estimated from the speech signal. Then, the speech spectrum is divided by the spectral envelope of the glottal source signal in order to remove the glottal source effects from the speech signal. Finally, the vocal tract transfer function is obtained by computing the spectral envelope of the resulting signal. In this work, the glottal source signal is represented using the Liljencrants-Fant model (LF-model). The experiments we present here show that the analysis-synthesis technique based on GSS can produce speech comparable to that of a high-quality vocoder that is based on the spectral envelope representation. However, it also permit control over voice qualities, namely to transform a modal voice into breathy and tense, by modifying the glottal parameters.

A major factor which causes a deterioration in speech quality in HMM-based speech synthesis is the use of a simple delta pulse signal to generate the excitation of voiced speech. This paper sets out a new approach to using an acoustic glottal source model in HMM-based synthesisers instead of the traditional pulse signal. The goal is to improve speech quality and to better model and transform voice characteristics. We have found the new method decreases buzziness and also improves prosodic modelling. A perceptual evaluation has supported this finding by showing a 55.6% preference for the new system, as against the baseline. This improvement, while not being as significant as we had initially expected, does encourage us to work on developing the proposed speech synthesiser further.

The assumptions we make about a dialogue partner's knowledge and communicative ability (i.e. our partner models) can influence our language choices. Although similar processes may operate in human-machine dialogue, the role of design in shaping these models, and their subsequent effects on interaction are not clearly understood. Focusing on synthesis design, we conduct a referential communication experiment to identify the impact of accented speech on lexical choice. In particular, we focus on whether accented speech may encourage the use of lexical alternatives that are relevant to a partner's accent, and how this is may vary when in dialogue with a human or machine. We find that people are more likely to use American English terms when speaking with a US accented partner than an Irish accented partner in both human and machine conditions. This lends support to the proposal that synthesis design can influence partner perception of lexical knowledge, which in turn guide user's lexical choices. We discuss the findings with relation to the nature and dynamics of partner models in human machine dialogue.

Graphical realism and the naturalness of the voice used are important aspects to consider when designing a virtual agent or character. In this work, we evaluate how synthetic speech impacts people's perceptions of a rendered virtual character. Using a controlled experiment, we focus on the role that speech, in particular voice expressiveness in the form of personality, has on the assessment of voice level and character level perceptions. We found that people rated a real human voice as more expressive, understandable and likeable than the expressive synthetic voice we developed. Contrary to our expectations, we found that the voices did not have a significant impact on the character level judgments; people in the voice conditions did not significantly vary on their ratings of appeal, credibility, humanlikeness and voice matching the character. The implications this has for character design and how this compares with previous work are discussed.

The great advantage of using a glottal source model in parametric speech synthesis is the degree of parametric flexibility it gives to transform and model aspects of voice quality and speaker identity. However, few studies have addressed how the glottal source affects the quality of synthetic speech.Here, we have developed the Glottal Spectral Separation (GSS) method which consists of separating the glottal source effects from the spectral envelope of the speech. It enables us to compare the LF-model with the simple impulse excitation, using the same spectral envelope to synthesize speech. The results of a perceptual evaluation showed that the LF-model clearly outperformed the impulse. The GSS method was also used to successfully transform a modal voice into a breathy or tense voice, by modifying the LF-parameters.The proposed technique could be used to improve the speech quality and source parametrization of HMM-based speech synthesizers, which use an impulse excitation.

No abstract

A great challenge for text-to-speech synthesis is to produce expressive speech. The main problem is that it is difficult to synthesise high-quality speech using expressive corpora. With the increasing interest in audiobook corpora for speech synthesis, there is a demand to synthesise speech which is rich in prosody, emotions and voice styles. In this work, Self-Organising Feature Maps (SOFM) are used for clustering the speech data using voice quality parameters of the glottal source, in order to map out the variety of voice styles in the corpus. Subjective evaluation showed that this clustering method successfully separated the speech data into groups of utterances associated with different voice characteristics. This work can be applied in unitselection synthesis by selecting appropriate data sets to synthesise utterances with specific voice styles. It can also be used in parametric speech synthesis to model different voice styles separately.