AUDIO & VOICE TECHNOLOGY
RESEARCH, DESIGN, DEVELOPMENT AND SERVICE OF AI-BASED VOICE AND AUDIO TECHNOLOGY
TUITO has developped solutions and expertise for building audio signal processing, analytics and voice interfaces tailored to the needs and constrains of its equipment manufacturer, operator and integrator customers.
Audio Signal Processing (ASP)
Replace costly hardware ASP by a versatile, low footprint, easily portable speech enhancement software component.
Sound Source Separation (SSS)
Filter, separate and enhance voice of concurrent speakers or implement echo cancellation.
Keyword/Wakeword Spotting (KWS)
Filter and extract keywords in real time from audio streams, for wake words, urgent words or simple commands detection.
Database Query by Voice (NL2Q)
Prompt queries to your private and confidential database using natural language.
Speech to Text and Natural Language Understanding (NLU)
For note taking or addition of vocal interaction to applications in true natural language and in all languages.
Sound Event Detection (SED)
Enhance environment perception adding Real-Time Sound Pattern and signature Recognition.
SOFTWARE DEVELOPMENT KITS (SDKs)
All our AI solutions have been packaged to be easily customized and integrated to our customer’s specific environments.
Contact us to find out more or to try our online demonstrator
Implementation
& use cases
Healthcare
Sound Event Detection (SED) is used to detect and classify sounds such as coughing, wheezing, or abnormal heartbeats in healthcare settings for remote patient monitoring and early warning systems. Audio Signal Processing (ASP) enhances the clarity of audio signals from medical devices like stethoscopes during telemedicine consultations, allowing for accurate diagnosis and treatment recommendations. Natural Language Understanding (NLU) assists healthcare professionals by transcribing and structuring spoken medical notes into electronic health records (EHRs), improving efficiency and accuracy. Keyword Spotting (KWS) can detect urgent situations and enhance staff security.
Manufacturing and Industry 4.0
Sound Event Detection (SED) is used to monitor industrial machinery for abnormal sounds, helping predict equipment failures and prevent costly downtime through predictive maintenance. Audio Signal Processing (ASP) can analyze audio signals produced during manufacturing processes to detect irregularities or defects in product quality, ensuring consistent production standards. Natural Language Understanding (NLU) allows workers to communicate with automated systems using voice commands, streamlining tasks and improving efficiency on the factory floor. Sound Event Detection (SED) can detect environmental sounds such as gas leaks, equipment malfunctions, or alarms in industrial settings, contributing to safety and compliance with environmental regulations.
Automotive Industry
Sound Event Detection (SED) is implemented in Advanced Driver Assistance Systems (ADAS) to detect sounds like sirens, horns, or screeching tires, enhancing road safety. Audio Signal Processing (ASP) technologies provide high-quality audio experiences for in-car infotainment systems, improving the overall driving experience. Sound Source Separation (SSS) isolates and filter the speech of concurrent speakers in the car for better interpersonal or man-machine vocal interactions.
Smart Home and IoT
Sound Event Detection (SED) is used in smart home security systems to detect sounds related to break-ins, glass breaking, or alarms, enhancing home security. Audio Signal Processing (ASP) enhances the voice recognition capabilities of smart speakers and voice-activated devices, improving user interactions.
Entertainment and Gaming
Sound Event Detection (SED) can be used to detect and respond to in-game sounds, making gameplay more immersive and dynamic.
Audio Signal Processing (ASP) enhances audio effects in video games and virtual reality experiences, creating more realistic and engaging environments.
Customer Service and Contact Centers
Sound Event Detection (SED) can identify specific sounds or keywords during customer service calls, helping companies monitor and improve service quality. Natural Language Understanding (NLU) is used to analyze customer interactions in real-time, extracting valuable insights for enhancing customer support and agent training.
Finance and Banking
Natural Language Understanding (NLU) is applied to analyze customer feedback and social media sentiment related to financial products and services, helping financial institutions make data-driven decisions. Natural Language to Query (NL2Q) provides user friendly prompting methods to interrogate professional and confidential databases.
