Learning to Understand Child-directed and Adult-directed Speech

Speech directed to children differs from adult-directed speech in linguistic aspects such as repetition, word choice, and sentence length, as well as in aspects of the speech signal itself, such as prosodic and phonemic variation. Human language acquisition research indicates that child-directed speech helps language learners. This study explores the effect of child-directed speech when learning to extract semantic information from speech directly. We compare the task performance of models trained on adult-directed speech (ADS) and child-directed speech (CDS). We find indications that CDS helps in the initial stages of learning, but eventually, models trained on ADS reach comparable task performance, and generalize better. The results suggest that this is at least partially due to linguistic rather than acoustic properties of the two registers, as we see the same pattern when looking at models trained on acoustically comparable synthetic speech.Comment: Authors found an error in preprocessing of transcriptions before they were fed to SBERT. After correction, the experiments were rerun. The updated results can be found in this version. Importantly, - Most scores were affected to a small degree (performance was slightly worse). - The effect was consistent across conditions. Therefore, the general patterns remain the sam

A Situational Analysis of Current Speech-Synthesis Systems for Child Voices: A Scoping Review of Qualitative and Quantitative Evidence

Background: Speech synthesis has customarily focused on adult speech, but with the rapid development of speech-synthesis technology, it is now possible to create child voices with a limited amount of child-speech data. This scoping review summarises the evidence base related to developing synthesised speech for children. (2) Method: The included studies were those that were (1) published between 2006 and 2021 and (2) included child participants or voices of children aged between 2–16 years old. (3) Results: 58 studies were identified. They were discussed based on the languages used, the speech-synthesis systems and/or methods used, the speech data used, the intelligibility of the speech and the ages of the voices. Based on the reviewed studies, relative to adult-speech synthesis, developing child-speech synthesis is notably more challenging. Child speech often presents with acoustic variability and articulatory errors. To account for this, researchers have most often attempted to adapt adult-speech models, using a variety of different adaptation techniques. (4) Conclusions: Adapting adult speech has proven successful in child-speech synthesis. It appears that the resulting quality can be improved by training a large amount of pre-selected speech data, aided by a neural-network classifier, to better match the children’s speech. We encourage future research surrounding individualised synthetic speech for children with CCN, with special attention to children who make use of low-resource languages

The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output

Speech synthesis : Developing a web application implementing speech technology

Speech is a natural media of communication for humans. Text-to-speech (TTS) technology uses a computer to synthesize speech. There are three main techniques of TTS synthesis. These are formant-based, articulatory and concatenative. The application areas of TTS include accessibility, education, entertainment and communication aid in mass transit. A web application was developed to demonstrate the application of speech synthesis technology. Existing speech synthesis engines for the Finnish language were compared and two open source text to speech engines, Festival and Espeak were selected to be used with the web application. The application uses a Linux-based speech server which communicates with client devices with the HTTP-GET protocol. The application development successfully demonstrated the use of speech synthesis in language learning. One of the emerging sectors of speech technologies is the mobile market due to limited input capabilities in mobile devices. Speech technologies are not equally available in all languages. Text in the Oromo language was tested using Finnish speech synthesizers; due to similar rules in orthography of germination of consonants and length of vowels, legible results were gained