Voice integrated systems

The program at Naval Air Development Center was initiated to determine the desirability of interactive voice systems for use in airborne weapon systems crew stations. A voice recognition and synthesis system (VRAS) was developed and incorporated into a human centrifuge. The speech recognition aspect of VRAS was developed using a voice command system (VCS) developed by Scope Electronics. The speech synthesis capability was supplied by a Votrax, VS-5, speech synthesis unit built by Vocal Interface. The effects of simulated flight on automatic speech recognition were determined by repeated trials in the VRAS-equipped centrifuge. The relationship of vibration, G, O2 mask, mission duration, and cockpit temperature and voice quality was determined. The results showed that: (1) voice quality degrades after 0.5 hours with an O2 mask; (2) voice quality degrades under high vibration; and (3) voice quality degrades under high levels of G. The voice quality studies are summarized. These results were obtained with a baseline of 80 percent recognition accuracy with VCS

The Army word recognition system

The application of speech recognition technology in the Army command and control area is presented. The problems associated with this program are described as well as as its relevance in terms of the man/machine interactions, voice inflexions, and the amount of training needed to interact with and utilize the automated system

Lampu Sein Helm Sepeda Berbasis Voice Recognition

Bicycles are not equipped with the turn signal. For driving safety, a bicycle helmet with a turn signal is designed with voice rrecognition. It is using the Arduino Nano as a controller to control the ON and OFF of turn signal lights with voice commands. This device uses a Voice Recognition sensor and microphone that placed on a bicycle helmet. When the voice command is mentioned in the microphone, the Voice Recognition sensor will detect the command specified, the sensor will automatically read and send a signal to Arduino, then the turn signal will light up as instructed, the Arduino on the helmet will send an indicator signal via the Bluetooth Module. The device is able to detect sound with a percentage of 80%. The tool can work with a distance of <2 meters with noise <71 db

DolphinAtack: Inaudible Voice Commands

Speech recognition (SR) systems such as Siri or Google Now have become an increasingly popular human-computer interaction method, and have turned various systems into voice controllable systems(VCS). Prior work on attacking VCS shows that the hidden voice commands that are incomprehensible to people can control the systems. Hidden voice commands, though hidden, are nonetheless audible. In this work, we design a completely inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers (e.g., f > 20 kHz) to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the speech recognition systems. We validate DolphinAttack on popular speech recognition systems, including Siri, Google Now, Samsung S Voice, Huawei HiVoice, Cortana and Alexa. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to switch the phone to the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions. We validate that it is feasible to detect DolphinAttack by classifying the audios using supported vector machine (SVM), and suggest to re-design voice controllable systems to be resilient to inaudible voice command attacks.Comment: 15 pages, 17 figure

Speech and Speaker Recognition for Home Automation: Preliminary Results

International audienceIn voice controlled multi-room smart homes ASR and speaker identification systems face distance speech conditionswhich have a significant impact on performance. Regarding voice command recognition, this paper presents an approach whichselects dynamically the best channel and adapts models to the environmental conditions. The method has been tested on datarecorded with 11 elderly and visually impaired participants in a real smart home. The voice command recognition error ratewas 3.2% in off-line condition and of 13.2% in online condition. For speaker identification, the performances were below veryspeaker dependant. However, we show a high correlation between performance and training size. The main difficulty was the tooshort utterance duration in comparison to state of the art studies. Moreover, speaker identification performance depends on the sizeof the adapting corpus and then users must record enough data before using the system

Automatic speech recognition research at NASA-Ames Research Center

A trainable acoustic pattern recognizer manufactured by Scope Electronics is presented. The voice command system VCS encodes speech by sampling 16 bandpass filters with center frequencies in the range from 200 to 5000 Hz. Variations in speaking rate are compensated for by a compression algorithm that subdivides each utterance into eight subintervals in such a way that the amount of spectral change within each subinterval is the same. The recorded filter values within each subinterval are then reduced to a 15-bit representation, giving a 120-bit encoding for each utterance. The VCS incorporates a simple recognition algorithm that utilizes five training samples of each word in a vocabulary of up to 24 words. The recognition rate of approximately 85 percent correct for untrained speakers and 94 percent correct for trained speakers was not considered adequate for flight systems use. Therefore, the built-in recognition algorithm was disabled, and the VCS was modified to transmit 120-bit encodings to an external computer for recognition

STS-41 Voice Command System Flight Experiment Report

This report presents the results of the Voice Command System (VCS) flight experiment on the five-day STS-41 mission. Two mission specialists,Bill Shepherd and Bruce Melnick, used the speaker-dependent system to evaluate the operational effectiveness of using voice to control a spacecraft system. In addition, data was gathered to analyze the effects of microgravity on speech recognition performance

RANCANG BANGUN PINTU OTOMATIS BERBASIS ARDUINO RFID DAN VOICE RECOGNITION ARDUINO

House doors typically consist of keys and keyholes to unlock them. However, with the evolution of technology, manual doors have transformed into automated systems. An automated door system simplifies looking mechanisms, reducing the number of keys required. However, many people are still unaware of automated doors designed using specific control systems. Therefore, this research aims to provide convenience to the public in utilizing this technology and implementing automatic doors using RFID and Voice Arduino. The research methodology employed a design and development approach involving several stages, beginning with problem identification, literature review, system planning, design, device testing, and concluding with report preparation. The design of the Arduino RFID and Voice Recognition-based automatic door system utilized RFID technology and Voice Recognition to control access to a specific area. In the this scheme, Arduino served as the central processing unit receiving information from RFID card readings to the authenticate valid cards. Additionally, it processed verified voice commands through The Voice Recognition module. This functionality allowed the door to open automatically upon detecting a registered card or upon issuing the correct voice command. This research primarily focuses on providing assistance to the community by simplifying access through voice codes and card-based authentication

Анализ влияния параметров обработки звукового сигнала на качество распознавания голосовых команд

В роботі розглянуто структуру системи розпізнавання голосових команд, алгоритм виділення мел-кепстральних коефіцієнтів та їх порівняння методом динамічного викривлення часу. В системі зі словником з п’ятдесяти команд вимовлених одним диктором було досліджено вплив на якість розпізнавання голосової команди таких параметрів як: частоти дискретизації, тривалості фрейму, кількості вибірок Фур’є, виду віконної функції на якість розпізнавання голосової команди.Introduction. Recognition of single (isolated) voice commands for the task of voice control over different devices is required. Typically, this control method requires high reliability (at least 95% accuracy voice recognition). It should be noted that voice commands are often pronounced in high noisiness. All presently known methods and algorithms of speech recognition do not allow clearly to determine which parameters of sound signal can provide the best results. The main part. On the first level of voice recognition (preprocessing and extracting of acoustic features that have a number of useful features) they are easily calculated, providing a compact representation of the voice commands that are resistant to noise interference. On the next level given command is looked for in the reference dictionary. Input file has to be divided into frames to get MFCC coefficients. Each frame is measured by a window function and processed by discrete Fourier transform. The resulting representation of signal in the frequency domain is divided into ranges using a set of triangular filters. The last step is to perform discrete cosine transform. Method of dynamic time warping allows to get a value, inverse of degree of similarity between given command and a reference. Conclusions. Research has shown that in the field of voice commands recognition optimum results in terms of quality / performance can be achieved using the following parameters of sound signal processing:8 kHz sample rate, frame duration 70-120 ms, Hamming weighting function of a window, number of Fourier samples is 512.В работе рассмотрено структуру системы распознавания голосовых команд, алгоритм выделения мел-кепстральных коэффициентов и их сравнение методом динамического искажения времени. В системе со словарем из пятидесяти команд произнесенных одним диктором было исследовано влияние на качество распознавания голосовых команд таких параметров как: частота дискретизации, продолжительность фрейма, количество выборок Фурье, вид оконной функции