Skip to main content

Speech technology


Speech technology is a type of computing technology that enables an electronic device to recognize, analyze and understand spoken word or audio. This is typically done by inputting digital sound signals and matching its pattern against a library of stored patterns.

Speech technology relies on the concepts of signal processing and machine learning. Signal processing is used to extract relevant information from speech, such as speaker characteristics, background noise and frequency. Machine learning is then applied to these listening devices to recognize and analyze the speech signals to perform the desired output, such as returning an automatic response.

Due to the fact that speech is a primary form of communication, the growth of speech technology is an important step towards harnessing unstructured voice data. Subfields of speech technology include speech recognition, speech verification, real time speech to text conversion, interactive voice response (IVR), speech synthesis and speech analytics.

Speech technology applications

With the progression of artificial intelligence solutions for enterprises, speech technology has many applications in all sectors, including law, healthcare, security, finance, enterprise and personal use. A few specific examples include:

  • Speech technology and speaker verification solutions can be applied to law enforcement techniques to identify and validate a person's voice.
  • Companies are able to apply speech technology such as speech to text and IVR functionalities to streamline customer service and support
  • Healthcare providers can use speech technology devices to aid patients that are visually impaired or hard of hearing.
  • Personal use voice assistants such as Siri, Google Home and Amazon Alexa are devices that offer individualized speech technology experiences.
  • Various careers, including speech technologists, speech engineers and speech pathologists, rely heavily on speech technology to collect more technical audio components like sample rate, signal to noise ratio and modulation.



Comments

Post a Comment

Popular posts from this blog

Understanding the Evolution: AI, ML, Deep Learning, and Gen AI

In the ever-evolving landscape of artificial intelligence (AI) and machine learning (ML), one of the most intriguing advancements is the emergence of General AI (Gen AI). To grasp its significance, it's essential to first distinguish between these interconnected but distinct technologies. AI, ML, and Deep Learning: The Building Blocks Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Machine Learning, a subset of AI, empowers machines to learn from data and improve over time without explicit programming. Deep Learning, a specialized subset of ML, involves neural networks with many layers (hence "deep"), capable of learning intricate patterns from vast amounts of data. Enter General AI (Gen AI): Unraveling the Next Frontier Unlike traditional AI systems that excel in specific tasks (narrow AI), General AI aims to replicate human cognitive abilities across various domains. I...
Post a Comment
Read more

Normalization of Database

Database Normalisation is a technique of organizing the data in the database. Normalization is a systematic approach of decomposing tables to eliminate data redundancy and undesirable characteristics like Insertion, Update and Deletion Anamolies. It is a multi-step process that puts data into tabular form by removing duplicated data from the relation tables. Normalization is used for mainly two purpose, Eliminating reduntant(useless) data. Ensuring data dependencies make sense i.e data is logically stored. Problem Without Normalization Without Normalization, it becomes difficult to handle and update the database, without facing data loss. Insertion, Updation and Deletion Anamolies are very frequent if Database is not Normalized. To understand these anomalies let us take an example of  Student  table. S_id S_Name S_Address Subject_opted 401 Adam Noida Bio 402 Alex Panipat Maths 403 Stuart Jammu Maths 404 Adam Noida Physics Updation Anamoly :  To upda...
Post a Comment
Read more

How to deal with a toxic working environment

Handling a toxic working environment can be challenging, but there are steps you can take to address the situation and improve your experience at work: Recognize the Signs : Identify the specific behaviors or situations that contribute to the toxicity in your workplace. This could include bullying, harassment, micromanagement, negativity, or lack of support from management. Maintain Boundaries : Set boundaries to protect your mental and emotional well-being. This may involve limiting interactions with toxic individuals, avoiding gossip or negative conversations, and prioritizing self-care outside of work. Seek Support : Reach out to trusted colleagues, friends, or family members for support and advice. Sharing your experiences with others can help you feel less isolated and provide perspective on the situation. Document Incidents : Keep a record of any incidents or behaviors that contribute to the toxic environment, including dates, times, and specific details. This documentation may b...
Post a Comment
Read more