Using speech recognition software a device can identify the words in your speech
Speech RecognitionLearn about the history of speech recognition and its various applications in the world today Show
What is speech recognition?Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability which enables a program to process human speech into a written format. While it’s commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice. IBM has had a prominent role within speech recognition since its inception, releasing of “Shoebox” in 1962. This machine had the ability to recognize 16 different words, advancing the initial work from Bell Labs from the 1950s. However, IBM didn’t stop there, but continued to innovate over the years, launching VoiceType Simply Speaking application in 1996. This speech recognition software had a 42,000-word vocabulary, supported English and Spanish, and included a spelling dictionary of 100,000 words. While speech technology had a limited vocabulary in the early days, it is utilized in a wide number of industries today, such as automotive, technology, and healthcare. Its adoption has only continued to accelerate in recent years due to advancements in deep learning and big data. Research (link resides outside IBM) shows that this market is expected to be worth $24.9 billion by 2025. Key features of effective speech recognitionMany speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning. They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, they learn as they go — evolving responses with each interaction. The best kind of systems also allow organizations to customize and adapt the technology to their specific requirements — everything from language and nuances of speech to brand recognition. For example:
Meanwhile, speech recognition continues to advance. Companies, like IBM, are making inroads in several areas, the better to improve human and machine interaction. Speech recognition algorithmsThe vagaries of human speech have made development challenging. It’s considered to be one of the most complex areas of computer science – involving linguistics, mathematics and statistics. Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output. Speech recognition technology is evaluated on its accuracy rate, i.e. word error rate (WER), and speed. A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Reaching human parity – meaning an error rate on par with that of two humans speaking – has long been the goal of speech recognition systems. Research from Lippmann (link resides outside IBM) (PDF, 344 KB) estimates the word error rate to be around 4 percent, but it’s been difficult to replicate the results from this paper. Read more on how IBM has made strides in this respect, achieving industry records in the field of speech recognition. Various algorithms and computation techniques are used to recognize speech into text and improve the accuracy of transcription. Below are brief explanations of some of the most commonly used methods:
Read on the Watson blog how IBM leverages SD models within their Speech to Text services. Speech recognition use casesA wide number of industries are utilizing different applications of speech technology today, helping businesses and consumers save time and even lives. Some examples include: Automotive: Speech recognizers improves driver safety by enabling voice-activated navigation systems and search capabilities in car radios. Technology: Virtual assistants are increasingly becoming integrated within our daily lives, particularly on our mobile devices. We use voice commands to access them through our smartphones, such as through Google Assistant or Apple’s Siri, for tasks, such as voice search, or through our speakers, via Amazon’s Alexa or Microsoft’s Cortana, to play music. They’ll only continue to integrate into the everyday products that we use, fueling the “Internet of Things” movement. Healthcare: Doctors and nurses leverage dictation applications to capture and log patient diagnoses and treatment notes. Sales: Speech recognition technology has a couple of applications in sales. It can help a call center transcribe thousands of phone calls between customers and agents to identify common call patterns and issues. Cognitive bots can also talk to people via a webpage, answering common queries and solving basic requests without needing to wait for a contact center agent to be available. It both instances speech recognition systems help reduce time to resolution for consumer issues. Security: As technology integrates into our daily lives, security protocols are an increasing priority. Voice-based authentication adds a viable level of security. Learn more how companies, such as Audioburst, are leveraging speech recognition software to index audio from radio stations and podcasts in real-time on our blog here. Speech Recognition and IBMIBM has pioneered the development of Speech Recognition tools and services that enable organizations to automate their complex business processes while gaining essential business insights.
For more information on how to get started with speech recognition technology, explore IBM Watson Speech to Text and IBM Watson Text to Speech. Sign up for an IBMid and create your IBM Cloud account. How does a speech recognition device work?The speech recognition software breaks the speech down into bits it can interpret, converts it into a digital format, and analyzes the pieces of content. It then makes determinations based on previous data and common speech patterns, making hypotheses about what the user is saying.
Can a machine identify words in sound?Many speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning. They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech.
Is an example of speech recognition software?The best voice recognition software turns speech to text, and understands spoken commands. Most people are familiar with personal assistants. These include Apple Siri, which came out first in 2011. Then there was Microsoft Cortana and Amazon Alexa which both came out in 2014.
Is speech recognition software or human?Speech recognition uses a process known as Natural Language Processing (or NLP) to allow a computer to simulate real human interaction. Nominally, what it does is take normal human speech and, using machine learning, respond in a way that mimics human responses.
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