Speech recognition software development

What is Healthcare Voice Recognition Software? This type of medical software converts voice inputs into traditional written documents. It automates tiresome and mistake-prone operational tasks, providing clinicians with a tool to create and submit medical records in seconds. To reduce the clinical documentation burden, different solutions are used in clinical practice: Medical scribes (traditional, in-person staff) Virtual scribes (remote workers, often located offshore) Medical speech recognition software (physicians must actively dictate notes) Ambient speech recognition software(which passively listens to doctor-patient interactions and extracts relevant medical details, focusing solely on automatic note generation) Artificial intelligence (AI) assistants (voice-controlled EHR helpers that doctors speak to directly, allowing the AI to perform commands such as documentation, EHR navigation, order entry, communication, and workflow automation). Medical Documentation Challenges Challenges without Modern Voice Recognition Software A huge administrative burden forces physicians to spend additional hours on documentation. The problem leads to increased rates of errors, higher possibility of psychological issues among staff, and worsened productivity.  Doctors perform multiple activities during their interactions with patients. They take symptoms, assess health factors, review previous reports and health history, as well as schedule additional tests and appointments with other specialists. Clinicians need a tool that would be able to perform several tasks: transcribe voice in real-time, comprehend subtleties of the medical context to take notes correctly, and understand doctors’ commands. Challenges with Legacy Voice Recognition Software The healthtech market offers various solutions. However, their functionality differs, limiting some of the clients’ expectations. Here are the challenges that prevent efficient clinical workflow while utilizing voice recognition software. Finding the balance between the speed and accuracy of the tool might be difficult. Some voice-based digital assistants offer excellent accuracy while being slow in functioning. Others perform faster, but they require manual editing. Both options are frustrating for doctors and may add to aggregating stress Manual editing demands time, forcing users to examine, edit, and submit the notes to the electronic health record (EHR), therefore preventing instant availability of the data Difficulties with managing the system might occur, as doctors’ voices vary in tone, pitch, and accent. Those variations affect the accuracy of transcription Hampered transition between commands and dictations makes the functionality of an integrated voice assistant inconvenient When manual editing is required, privacy issues might occur. Those staff members who check the accuracy of the records need access to sensitive patient data, creating additional risk.  AI-assisted Voice Recognition Software Functionality Modern voice recognition tools are meant to become “invisible” doctors’ digital assistants. Such assistants provide clinicians with necessary patient data, accurately make notes, and answer special queries. The software usually performs the following features: Workflow integration and usability features Fit into the workflows of each individual clinician and understand different medical specialties, from behavioral health to surgery Combine voice dictations and commands in one tool with a seamless transition from the prior to the latter to enable clinicians to deal with documentation, coding, and looking through the data in the EHR  Analyze user’s intentions, navigation events, or cursor movements, allowing doctors to naturally dictate the data and then request details to fill in documentation sections if necessary  Comprehend the nuances of the country-specific healthcare environment  Provide additional assistance with answering inbox messages, compiling referral letters, and other administrative tasks. Data integration and accuracy features Integrate a large language model (LLM) to learn patient data, health history, and other relevant contexts to help doctors create notes Integrate the data with key systems, such as EHR, in two directions, i.e., using the EHR data to generate the note and leveraging the actual note to complete the fields in the EHR. Combine the information about medical context, i.e., patient details, doctor’s specialty, type of appointment, etc., with medically customized automatic speech recognition to select relevant terms and generate highly accurate notes. Keep a record of the notes, track and understand the text dictated at various sections, including situations involving issues like slow wifi or poor-quality microphones  Compile patient summaries using automated LLM-based summarization workflows to enable practitioners to prepare for patient visits and analyze data from previous emergency department visits, inpatient visits, and appointments with specialists   Benefits of modern AI-assisted Voice Recognition Software Healthcare systems that have already utilized AI and medical speech recognition products report the following improvements: 79% of users reported better documentation quality, 70% saw reduced burnout and fatigue, 81% of patients saw greater physician focus, 72% reduction in documentation time, 40% decrease in after-hours work, including weekend work, 20% increase in practice satisfaction, according to the research Single solution instead of applying several ones Supporting a broad number of clinicians of various specialties Allowing doctors to have a natural interaction with their colleagues and patients in the decision-making process Easy switching between dictation, request, command, or query options and relevant accuracy while performing those actions Elimination of the necessity to type and click while compiling documentation, resulting in high doctors’ speed How Belitsoft Can Help Healthcare software development companies like Belitsoft help healthtech startups create, customize and support speech recognition software solutions with the following capabilities: Real-time voice transcription for doctors to dictate notes Seamless switching between taking notes and processing voice commands Automatic synchronization of doctors’ notes with internal systems (EHRs, etc.) Customized note sections for doctors to easily navigate through medication history, health maintenance, etc. Embedded personalized templates for doctors Integrated ICD-10 diagnosis codes to enable automatic coding and billing Compliance with health systems’ requirements Multi-language interface Augmentation with a Google Chrome extension which helps to create notes conveniently and more. If you're looking for expert assistance in data infrastructure, data platforms, workflow engineering, and cloud development (including AWS, Azure, and Google Cloud), as well as hybrid or on-premises environments, Belitsoft healthcare software development company offers outsourced expertise to meet your needs.

What is Speech2Face? Speech2Face (S2F) is a neural network or an AI algorithm trained to determine the gender, age, and ethnicity of a speaker by their voice. This system is also able to recreate an approximate portrait of a person from a speech sample. Source: https://arxiv.org/pdf/1905.09773.pdf How does Speech2Face work? To train Speech2Face researchers used more than a million YouTube videos. Analyzing the images in the database, the system revealed typical correlations between facial traits and voices and learned to detect them. S2F includes two main components: A voice encoder. As input, it takes a speech sample computed into a spectrogram - a visual representation of sound waves. Then the system encodes it into a vector with determined facial features. A face decoder. It takes the encoded vector and reconstructs the portrait from it. The image of the face is generated in standard form (front side and neutral by expression). Source: https://arxiv.org/pdf/1905.09773.pdf Why does Speech2Face work? MIT’s research team explains in detail why the idea of recreating a face just by voice works: “There is a strong connection between speech and appearance, part of which is a direct result of the mechanics of speech production: age, gender (which affects the pitch of our voice), the shape of the mouth, facial bone structure, thin or full lips—all can affect the sound we generate”. Testing and results assessment Speech2Face was tested using qualitative and quantitative metrics. Scientists compared the traits of people in a video with their portraits created by voice. To evaluate the results researchers constructed a classification matrix. Source: https://arxiv.org/pdf/1905.09773.pdf Demographic attributes It turned out that S2F copes successfully with pinpointing gender. However, it is far off determining age correctly and, on average, is mistaken for ± 10 years. Also, it was found that the algorithm recreates Europeans’ and Asians’ features best of all. Source: https://arxiv.org/pdf/1905.09773.pdf Craniofacial attributes Using a face attribute classifier, the researchers revealed that several craniofacial traits were reconstructed well. The best match was found for the nasal index and nose width. Source: https://arxiv.org/pdf/1905.09773.pdf The impact of input audio duration The researchers decided to increase the length of voice recordings from 3 seconds to 6. This measure significantly improved the S2F reconstruction results, since the facial traits were captured better. Source: https://arxiv.org/pdf/1905.09773.pdf Effect of language and accent When S2F listened to an Asian male talking in English and Chinese, it reconstructed two different faces, one Asian and the second European. However, the neural network successfully identified an Asian girl speaking English and recreated a face with oriental features. So in case of language variations mixed performance was observed. Source: https://arxiv.org/pdf/1905.09773.pdf Mismatches and Feature similarity The researchers emphasize that S2F doesn’t reveal accurate representations of any individual but creates “average-looking faces.” So it can’t produce the persons’ image similar enough that you'd be able to recognize someone. MIT’s researchers give explanations to this: “The goal was not to predict a recognizable image of the exact face, but rather to capture dominant facial traits of the person that are correlated with the input speech.” Given that the system is still at the initial stage, it’s occasionally mistaken. Here are examples of some fails: Source: https://arxiv.org/pdf/1905.09773.pdf Ethical questions MIT’s researchers outspoken some ethical considerations about Speech2Face: “The training data we use is a collection of educational videos from YouTube, and does not represent equally the entire world population … For example, if a certain language does not appear in the training data, our reconstructions will not capture well the facial attributes that may be correlated with that language.” So to enhance the Speech2Face results, researchers need to collect a complete database. It should represent people of different races, nationalities, levels of education, places of residence and therefore accents and languages. Use Case - Speech-to-cartoon S2F-made portraits could be used for creating personalized cartoons. For example, researchers did it with the help of GBoard app. Source: https://arxiv.org/pdf/1905.09773.pdf Such cartoon-like faces can be used as a profile picture during a phone or video call. Especially, if a speaker prefers not to share a personal photo. Also, animated faces can be directly assigned to voices used in virtual assistants and other smart home systems & devices. Potential Use Cases If the Spech2Face is trained further it could be useful for: Security forces and law enforcement agencies. Exact speech-to-face reconstructions can help to catch the criminals. For example, in the case of a masked bank robbery, phoned-in threats from terrorists or extortions from kidnappers. Media and motion picture industry. This technology can help VX (viewer experience) designers to build mind-blowing effects. Conclusion Presently Speech2Face appears to be an effective gender, age, and ethnicity classifier by voice. But this technology is also notable as it takes AI to another level. Perhaps, after further training, S2F will be able to predict the exact person’s face by voice. So it’s safe to say that we are witnessing a great breakthrough in the tech world and it’s exciting.