Development of a Field-Deployable Voice-Controlled Ultrasound Scanner System

Modern ultrasound scanners are portable and have become very useful for clinical diagnosis. However, they have limitations for field use purposes, primarily because they occupy both hands of the physician who performs the scanning. The goal of this thesis is to develop a wearable voice-controlled ultrasound scanner system that would enable the physician to provide a fast and efficient diagnosis. This is expected to become very useful for emergency and trauma applications. A commercially available ultrasound scanner system, Terason 2000, was chosen as the basis for development. This system consists of a laptop, a hardware unit containing the RF beamforming and signal processing chips and the ultrasound transducer. In its commercial version, the control of the ultrasound system is performed via a Graphical User Interface with a Windows-application look and feel. In the system we developed, a command and control speech recognition engine and a noise-canceling microphone are selected to control the scanner using voice commands. A mini-joystick is attached to the top of the ultrasound transducer for distance and area measurements and to perform zooming of the ultrasound images. An eye-wear viewer connected to the laptop enables the user to view the ultrasound images directly. Power management features are incorporated into the ultrasound system in order to conserve the battery power. A wireless connection is set up with a remote laptop to enable real-time transmission of wireless images. The result is a truly untethered, voice-controlled, ultrasound system enclosed in a backpack and monitored by the eye-wear viewer. (In the second generation of this system, the laptop is replaced by an embedded PC and is incorporated into a photographer’s vest). The voice-controlled system has to be made reliable under various forms of background noise. Three command and control speech recognition systems were selected and their recognition performances were determined under different types and levels of ambient noise. The variation of recognition rates was also analyzed over 6 different speakers. A detailed testing was also conducted to identify the ideal combination of a microphone and speech recognition engine suitable for the ultrasound scanner system. Six different microphones, each with their own unique methods of implementing noise cancellation features, were chosen as candidates for this analysis. The testing was conducted by making recordings inside a highly reverberant acoustic noisy chamber, and the recordings were fed to the automatic speech recognition engines offline for performance evaluation. The speech recognition engine and microphone selected as a result of this extensive testing were then incorporated into the wearable ultrasound scanner system. This thesis also discusses the implementation of the human-speech interface, which also plays a major role in the effectiveness of the voice-controlled ultrasound scanner system

On Distant Speech Recognition for Home Automation

The official version of this draft is available at Springer via http://dx.doi.org/10.1007/978-3-319-16226-3_7International audienceIn the framework of Ambient Assisted Living, home automation may be a solution for helping elderly people living alone at home. This study is part of the Sweet-Home project which aims at developing a new home automation system based on voice command to improve support and well-being of people in loss of autonomy. The goal of the study is vocal order recognition with a focus on two aspects: distance speech recognition and sentence spotting. Several ASR techniques were evaluated on a realistic corpus acquired in a 4-room flat equipped with microphones set in the ceiling. This distant speech French corpus was recorded with 21 speakers who acted scenarios of activities of daily living. Techniques acting at the decoding stage, such as our novel approach called Driven Decoding Algorithm (DDA), gave better speech recognition results than the baseline and other approaches. This solution which uses the two best SNR channels and a priori knowledge (voice commands and distress sentences) has demonstrated an increase in recognition rate without introducing false alarms

Distant speech recognition for home automation: Preliminary experimental results in a smart home

International audienceThis paper presents a study that is part of the Sweet-Home project which aims at developing a new home automation system based on voice command. The study focused on two tasks: distant speech recognition and sentence spotting (e.g., recognition of domotic orders). Regarding the first task, different combinations of ASR systems, language and acoustic models were tested. Fusion of ASR outputs by consensus and with a triggered language model (using a priori knowledge) were investigated. For the sentence spotting task, an algorithm based on distance evaluation between the current ASR hypotheses and the predefine set of keyword patterns was introduced in order to retrieve the correct sentences in spite of the ASR errors. The techniques were assessed on real daily living data collected in a 4-room smart home that was fully equipped with standard tactile commands and with 7 wireless microphones set in the ceiling. Thanks to Driven Decoding Algorithm techniques, a classical ASR system reached 7.9% WER against 35% WER in standard configuration and 15% with MLLR adaptation only. The best keyword pattern classification result obtained in distant speech conditions was 7.5% CER

Configurable EBEN: Extreme Bandwidth Extension Network to enhance body-conducted speech capture

This paper presents a configurable version of Extreme Bandwidth Extension Network (EBEN), a Generative Adversarial Network (GAN) designed to improve audio captured with body-conduction microphones. We show that although these microphones significantly reduce environmental noise, this insensitivity to ambient noise happens at the expense of the bandwidth of the speech signal acquired by the wearer of the devices. The obtained captured signals therefore require the use of signal enhancement techniques to recover the full-bandwidth speech. EBEN leverages a configurable multiband decomposition of the raw captured signal. This decomposition allows the data time domain dimensions to be reduced and the full band signal to be better controlled. The multiband representation of the captured signal is processed through a U-Net-like model, which combines feature and adversarial losses to generate an enhanced speech signal. We also benefit from this original representation in the proposed configurable discriminators architecture. The configurable EBEN approach can achieve state-of-the-art enhancement results on synthetic data with a lightweight generator that allows real-time processing.Comment: Accepted in IEEE/ACM Transactions on Audio, Speech and Language Processing on 14/08/202

Biologically inspired speaker verification

Speaker verification is an active research problem that has been addressed using a variety of different classification techniques. However, in general, methods inspired by the human auditory system tend to show better verification performance than other methods. In this thesis three biologically inspired speaker verification algorithms are presented

HeadScan: A Wearable System for Radio-Based Sensing of Head and Mouth-Related Activities

The popularity of wearables continues to rise. However, possible applications, and even their raw functionality are constrained by the types of sensors that are currently available. Accelerometers and gyroscopes struggle to capture complex user activities. Microphones and image sensors are more powerful but capture privacy sensitive information. Physiological sensors are obtrusive to users as they often require skin contact and must be placed at certain body positions to function. In contrast, radio-based sensing uses wireless radio signals to capture movements of different parts of the body, and therefore provides a contactless and privacy-preserving approach to detect and monitor human activities. In this paper, we contribute to the search for new sensing modalities for the next generation of wearable devices by exploring the feasibility of mobile radiobased human activity recognition. We believe radio-based sensing has the potential to fundamentally transform wearables as we currently know them. As the first step to achieve our vision, we have designed and developed HeadScan, a first-of-its-kind wearable for radio-based sensing of a number of human activities that involve head and mouth movements. HeadScan only requires a pair of small antennas placed on the shoulder and collar and one wearable unit worn on the arm or the belt of the user. Head- Scan uses the fine-grained CSI measurements extracted from radio signals and incorporates a novel signal processing pipeline that converts the raw CSI measurements into the targeted human activities. To examine the feasibility and performance of HeadScan, we have collected approximate 50.5 hours data from seven users. Our wide-ranging experiments include comparisons to a conventional skin-contact audio-based sensing approach to tracking the same set of head and mouth-related activities. Our experimental results highlight the enormous potential of our radio-based mobile sensing approach and provide guidance to future explorations

Cough Monitoring Through Audio Analysis

The detection of cough events in audio recordings requires the analysis of a significant amount of data as cough is typically monitored continuously over several hours to capture naturally occurring cough events. The recorded data is mostly composed of undesired sound events such as silence, background noise, and speech. To reduce computational costs and to address the ethical concerns raised from the collection of audio data in public environments, the data requires pre-processing prior to any further analysis. Current cough detection algorithms typically use pre-processing methods to remove undesired audio segments from the collected data but do not preserve the privacy of individuals being recorded while monitoring respiratory events. This study reveals the need for an automatic pre-processing method that removes sensitive data from the recording prior to any further analysis to ensure privacy preservation of individuals. Specific characteristics of cough sounds can be used to discard sensitive data from audio recordings at a pre-processing stage, improving privacy preservation, and decreasing ethical concerns when dealing with cough monitoring through audio analysis. We propose a pre-processing algorithm that increases privacy preservation and significantly decreases the amount of data to be analysed, by separating cough segments from other non-cough segments, including speech, in audio recordings. Our method verifies the presence of signal energy in both lower and higher frequency regions and discards segments whose energy concentrates only on one of them. The method is iteratively applied on the same data to increase the percentage of data reduction and privacy preservation. We evaluated the performance of our algorithm using several hours of audio recordings with manually pre-annotated cough and speech events. Our results showed that 5 iterations of the proposed method can discard up to 88.94% of the speech content present in the recordings, allowing for a strong privacy preservation while considerably reducing the amount of data to be further analysed by 91.79%. The data reduction and privacy preservation achievements of the proposed pre-processing algorithm offers the possibility to use larger datasets captured in public environments and would beneficiate all cough detection algorithms by preserving the privacy of subjects and by-stander conversations recorded during cough monitoring