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One of the major factors that affect the acceptance of robots in Human-Robot Interaction applications is the type of voice with which they interact with humans. The robot’s voice can be used to express empathy, which is an affective response of the robot to the human user. In this study, the aim is to find out if social robots with empathetic voice are acceptable for users in healthcare applications. A pilot study using an empathetic voice spoken by a voice actor was conducted. Only prosody in speech is used to express empathy here, without any visual cues. Also, the emotions needed for an empathetic voice are identified. It was found that the emotions needed are not only the stronger primary emotions, but also the nuanced secondary emotions. These emotions are then synthesised using prosody modelling. A second study, replicating the pilot test is conducted using the synthesised voices to investigate if empathy is perceived from the synthetic voice as well. This paper reports the modelling and synthesises of an empathetic voice, and experimentally shows that people prefer empathetic voice for healthcare robots. The results can be further used to develop empathetic social robots, that can improve people’s acceptance of social robots.

Te reo Māori (referred to as Māori), New Zealand's indigenous language, is under-resourced in language technology. Māori speakers are bilingual, where Māori is code-switched with English. Unfortunately, there are minimal resources available for Māori language technology, language detection and code-switch detection between Māori-English pair. Both English and Māori use Roman-derived orthography making rule-based systems for detecting language and code-switching restrictive. Most Māori language detection is done manually by language experts. This research builds a Māori-English bilingual database of 66,016,807 words with word-level language annotation. The New Zealand Parliament Hansard debates reports were used to build the database. The language labels are assigned using language-specific rules and expert manual annotations. Words with the same spelling, but different meanings, exist for Māori and English. These words could not be categorised as Māori or English based on word-level language rules. Hence, manual annotations were necessary. An analysis reporting the various aspects of the database such as metadata, year-wise analysis, frequently occurring words, sentence length and N-grams is also reported. The database developed here is a valuable tool for future language and speech technology development for Aotearoa New Zealand. The methodology followed to label the database can also be followed by other low-resourced language pairs., Submitted to Springer Language Resources and Evaluation Journal 2022

An alternative design of masker for real-time speech masking is proposed. Time-reversed speech, which is also adopted in the proposed design, has been commonly used for speech masking because of its efficient performance in making the target speech unintelligible using informational masking. However, previous studies revealed that a time-reversed speech causes more annoyance and cognitive distraction for the listeners. Meanwhile, although generating time-reversed speech from the target speech would help improve masking effectiveness, it would require real-time processing as the target speech would not be available beforehand. The proposed masker design utilises techniques known for minimising discontinuities in the waveform of synthesised speech in order to minimise annoyance and distraction to the listeners. The design also avoids processes that hinders real-time generation of the masker. Results of subjective listening tests reveal that the proposed design is able to compromise the level of annoyance and the masking effect. Only marginal improvement is observed in the level of distraction. Further attempt to mitigate annoyance of the proposed masker by adding artificial reverberation did not help as it also reduces the masking effect significantly.

From the times of the British raj to this date, scavenging rail tracks has been a puzzling social issue. The fact that humans have to clean human waste and other garbage thrown on rail tracks is a situation that needs immediate remedy. Manual scavenging is done now-a-days, owing to the peculiar nature of the job, and many who are engaged for this job suffer from related health problems. The prevailing condition can be rectified to some extent by the adequate use of robotics and control technology. The proposed idea of automatic railway track cleaning system comprises an automatic vehicle that goes on land and track. This proposed intelligent machine specially designed for the Indian railways can clean the railway tracks in a systematic manner. It consists of a four-wheel running robot with a suction unit, cleaning unit, automatic displacement unit, an intelligent control system, an intelligent train sensing unit, and power unit. This device is the first of its kind proposed to be developed exclusively for the Indian Railways. Also, the railways can save a lot of money on water and labour charges. The application of this project in the current railway cleaning scenario will ensure that there will not be any nauseating scenes at railway stations across the country. We have accomplished some functionality critical in the waste clean-up in railway tracks, and have also tried to find solution for connected problems.

In this paper a CART-based pause duration prediction model has been developed for Malayalam language. Prosodic features like pause durations, syllable prolongations etc. play an important role in making the speech output from a Text To Speech (TTS) system more intelligible. An analysis on the various factors that affect pause duration for Malayalam language has not been conducted till date. Here, inferential and descriptive statistical analysis tools are used to analyze the effect of various factors on pause duration for Malayalam. These identified factors are then used to model pause duration for pause after a word, pause after a phrase, pause after a comma and pause after a sentence separately. The RMSE and correlation values are calculated to evaluate the correctness of the model.