Navigation Aid for the Blind and the Visually Impaired People using eSpeak and Tensor Flow

Applications of science and technology have made a human life much easier. Vision plays a very important role in one’s life. Disease, accidents or due some other reasons people may loose their vision. Navigation becomes a major problem for the people with complete blindness or partial blindness. This paper aims to provide navigation guidance for visually impaired. Here we have designed a model which provides the instruction for the visionless people to navigate freely. NoIR camera is used to capture the picture around the person and identifies the objects. Using earphones voice output is provided defining the objects. This model includes Raspberry Pi 3 processor which collects the objects in surroundings and converts them into voice message, NoIR camera is used detect the object, power bank provides the power and earphones are used here the output message. TensorFlow API an open source software library used for object detection and classification. Using TensorFlow API multiple objects are obtained in a single frame. eSpeak a Text to Speech synthesizer (TTS) software is used to convert text (detected objects) to speech format. Hence using NoIR camera video which is captured is converted into voice output which provides the guidance for detecting objects. Using COCO model 90 commonly used objects are identified like person, table, book etc.

A unit selection text-to-speech-and-singing synthesis framework from neutral speech: proof of concept

Text-to-speech (TTS) synthesis systems have been widely used in general-purpose applications based on the generation of speech. Nonetheless, there are some domains, such as storytelling or voice output aid devices, which may also require singing. To enable a corpus-based TTS system to sing, a supplementary singing database should be recorded. This solution, however, might be too costly for eventual singing needs, or even unfeasible if the original speaker is unavailable or unable to sing properly. This work introduces a unit selection-based text-to-speech-and-singing (US-TTS&S) synthesis framework, which integrates speech-to-singing (STS) conversion to enable the generation of both speech and singing from an input text and a score, respectively, using the same neutral speech corpus. The viability of the proposal is evaluated considering three vocal ranges and two tempos on a proof-of-concept implementation using a 2.6-h Spanish neutral speech corpus. The experiments show that challenging STS transformation factors are required to sing beyond the corpus vocal range and/or with notes longer than 150 ms. While score-driven US configurations allow the reduction of pitch-scale factors, time-scale factors are not reduced due to the short length of the spoken vowels. Moreover, in the MUSHRA test, text-driven and score-driven US configurations obtain similar naturalness rates of around 40 for all the analysed scenarios. Although these naturalness scores are far from those of vocaloid, the singing scores of around 60 which were obtained validate that the framework could reasonably address eventual singing needs.