What is AI speech recognition? Explaining the basic concepts and use cases!

Basic knowledge of voice recognition systems

About the voice recognition system

Speech recognition is a technology in which a computer analyzes spoken words and understands and processes them as text information or operational commands. Speech recognition systems capture speech as a digital signal, analyze it using language models and machine learning algorithms, and convert it into meaningful information. In recent years, advances in AI technology have dramatically improved recognition accuracy and response speed, making it widely used in a variety of situations in everyday life and business.

Specifically, it is used in smartphone voice assistants (e.g., Apple's Siri), automated response systems for customer support, automatic transcription of phone conversations, voice-activated web searches, etc. These speech recognition systems are attracting attention as solutions that facilitate smooth communication with customers and contribute greatly to improving the efficiency of corporate operations.

For this reason, voice recognition has become an essential technology in modern society. Voice interfaces play an important role, especially in situations where contactless operation is required or for users with visual or physical limitations. Deepening your knowledge of voice recognition will be of great value in future technology applications and business strategies.

How AI voice recognition works

First of all, AI speech recognition, as the name suggests, is speech recognition using AI. The mechanism of AI speech recognition is the collaboration of multiple advanced technologies, from inputting voice data to converting it into text. First, the voice spoken by the user is acquired as digital voice data through a device such as a microphone. At this stage, the sound waveform is digitized and converted into a format that a computer can process.

The speech recognition system then preprocesses this data by removing noise, adjusting volume, extracting features of the speaker's voice, and preparing it for improved recognition. The speech is then broken down into the smallest units of language, called "phonemes," which are then matched against a language model to convert them into meaningful words and sentences.

Machine learning technology is at the heart of this process. By studying large amounts of voice data and its corresponding text, AI can accurately detect differences in pronunciation, speaking habits, and infer meaning based on context. Deep learning models in particular enable more flexible and accurate recognition than traditional speech recognition without AI, and are widely applied to functions such as chatbots and automatic call response.

As you can see, AI speech recognition is not just a sound conversion, but an advanced technology that involves complex processing and learning. Understanding how it works can help companies make decisions when adopting it, and it can easily reveal its potential in improving customer service and business efficiency.

Differences from conventional voice recognition

Speech recognition technology has continued to evolve over the years, but the AI speech recognition that is currently mainstream has many different characteristics from conventional methods . Here, we will clarify the differences between the two and explain the evolution of the technology and its background.

Feature Comparison

Conventional speech recognition technology is built on a method of analyzing speech based on predefined rules and vocabulary dictionaries, and is basically designed to support a limited range of vocabulary and grammatical structures. This method uses a "pattern matching" technique, which compares features extracted from speech with pre-registered speech patterns and converts them into corresponding text.

However, this method has limitations in terms of flexibility, and recognition accuracy tends to drop significantly when the phrasing or pronunciation used by the speaker differs from those defined in advance. In particular, when words with the same meaning are expressed differently, or when there are individual differences in speaking style, accurate recognition is often difficult, limiting its practicality.

Due to these limitations, conventional speech recognition is often limited to specific applications and environments, and is considered unsuitable for handling a wide range of conversations and natural dialogue.

On the other hand, AI speech recognition uses machine learning and deep learning to learn language patterns from vast amounts of voice data. This allows it to flexibly understand the speaker's accent, differences in phrasing, and conversational context, enabling more natural recognition. It also has a high ability to combine multiple pieces of information to make judgments, and is compatible with advanced functions such as chatbots and machine translation.

Advantages and disadvantages

Technological evolution

Speech recognition technology has evolved from its early rule-based approach to statistical methods, and then to its current AI-based approach. Since the 2010s, the advent of deep learning has dramatically improved the accuracy of speech recognition. AI speech recognition is now used in a variety of fields , including smart speakers, automated call answering systems, and medical record-keeping support.

This evolution is more than just a technological innovation; it has the potential to dramatically change the way we live and do business.

Technical aspects of voice recognition

AI speech recognition systems are built by combining multiple technical elements to accurately understand and convert speech. This section explains the basic process, from acquiring acoustic signals to extracting features and analyzing them using acoustic models.

Acoustic analysis and acoustic models

Acoustic signal acquisition method

The first step in speech recognition is acquiring an acoustic signal. Typically, a microphone is used to record the speaker's voice and convert the analog audio into digital audio. This digital audio is treated as time-series data and passed on to subsequent processing. Nowadays, a variety of devices, including smartphones, video apps, and conferencing systems, have this functionality.

Feature extraction

Because voice data is vast and difficult to handle as is, a process called feature extraction is performed. This is the process of extracting only meaningful information from the voice and converting it into numerical values. A typical method is the "Mel Frequency Cepstrum Coefficient (MFCC)," which extracts voice features as numerical values based on the mechanisms of human hearing. This process makes it possible to analyze the content of the voice more efficiently and accurately.

The role of acoustic models

In speech recognition, the extracted features are passed to an acoustic model . This model learns the relationship between the characteristics of speech and phonemes (the smallest units that make up language) and identifies which sounds correspond to which letters or words. In recent years, neural network-type acoustic models that utilize deep learning have become mainstream, and are able to analyze speech with higher recognition accuracy than traditional statistical methods.

As such, speech recognition is not simply recording and transcribing text; it is the result of the collaboration of multiple advanced technologies, from detailed analysis of acoustic signals to model design and operation. With technological advances, it is expected that even more accurate and real-time speech recognition will be realized in the future.

Language Model and Text Output

When a speech recognition system converts speech into text , it doesn't simply listen to the sounds, but uses advanced language modeling to output natural-sounding sentences that are easy for users to read and understand.

Language model types

A language model is a mechanism that learns the word arrangement and grammatical patterns in a sentence and organizes the results of speech recognition into sentences. Traditionally, statistical methods such as n-gram models , which predict the next word based on the previous "n" words, were mainstream. However, neural network-based models are now mainstream, making it possible to understand more complex contexts and meanings.

Context-aware processing

In AI speech recognition, processing that takes into account the context before and after the utterance plays an important role, rather than just recognizing individual words. For example, the same speech may be recognized as different characters or words depending on the flow of the conversation and the speaker's intention. By taking this context into account, more natural and accurate text output is possible, even in languages like Japanese, where meaning is easily influenced by context.

Improved accuracy of text output

To improve the accuracy of text output, it is essential to train using large amounts of data . Speech recognition systems analyze speech data containing various speaking styles, accents, and technical terms to improve the accuracy of their models. This enables stable recognition even in situations that require a high level of text conversion , such as generating subtitles for videos or responding to chatbots.

End-to-End System Using AI

The end-to-end model in AI speech recognition is an innovative approach that processes everything from voice input to text output. It eliminates the need to build and manage traditional acoustic and language models separately, and offers the major advantage of simplifying data processing and streamlining the learning process .

With this method, the entire process, from directly inputting voice data into an AI model to outputting it as text, is carried out in a single integrated system. This eliminates the need for complex settings and effort, while enabling natural text conversion that is close to human conversation . Furthermore, by allowing the AI to learn from large amounts of data, it becomes possible to automatically provide highly accurate results with fewer misrecognitions.

The end-to-end model provides strong support for improving business efficiency by linking with multiple business systems , such as chatbots, logistics management, and customer support. Advances in AI-driven speech recognition are expected to be used in even more fields in the future.

Benefits and applications of AI voice recognition

Benefits of AI voice recognition

AI speech recognition has many benefits . The first thing to note is the speed at which it can convert speech into text . Compared to traditional manual input, speech can be converted into text in real time, which greatly improves the efficiency of data entry . This dramatically increases business productivity.

AI speech recognition is also excellent in that it reduces the amount of work required by humans . For example, there is no need to re-listen to recordings of meetings to create minutes later; instead, the results can be checked easily . Furthermore, AI can handle differences in pronunciation and speaking habits, minimizing the burden on users.

Another feature is that it can easily support multiple languages . Since it can support not only Japanese but also various languages such as English and Chinese, it is extremely useful in international business and communication situations. Another attractive feature is that anyone can use it easily by simply downloading the dedicated app.

AI voice recognition applications

AI speech recognition is used in a variety of settings and industries in our daily lives and businesses.

Customer Support

By equipping call centers and chat assistants with an automatic response function that uses AI speech recognition, customer inquiries can be responded to quickly and accurately, which is expected to reduce the number of operators, shorten response times, and improve customer satisfaction.

medical settings

By allowing doctors to record audio during consultations, the system will improve work efficiency . It is gaining attention as a tool to support human work , such as automatically creating medical records and recording conversations with patients.

Education

By utilizing speech recognition in online classes and language learning apps, it becomes possible to check pronunciation and generate automatic subtitles, which will increase learners' comprehension and improve the quality of education.

Creating minutes and using translation functions

AI speech recognition also contributes greatly to the efficiency of meetings . By recording and converting what is said during a meeting in real time and automatically creating minutes , the effort required for recording can be significantly reduced.

In addition, by utilizing the real-time translation function , there are an increasing number of situations where interpretation is no longer necessary at international conferences or with multinational teams. AI instantly translates , facilitating the creation of documents and smooth communication according to the purpose .

Furthermore, recording using voice recognition is highly accurate , preventing misunderstandings due to mishearing or misremembering, making it possible to accurately record and share the contents of meetings .

Challenges Facing AI Speech Recognition

Dealing with dialects and special expressions

As AI speech recognition becomes more widespread, one of the major challenges is how to handle dialects and regional vocabulary. Japanese has a variety of dialects, including Kansai, Tohoku, and Kyushu, each with its own unique vocabulary and intonation. To accurately recognize these dialects, it is essential to build a model that can understand expressions rooted in specific regions and cultures.

For example, if the words used in meetings or conversations vary by region, the AI may give incorrect answers. For this reason, it is important to collect speech data that includes dialects and train the model. In addition, incorporating dictionaries and translation functions customized to the user's needs will enable more natural conversations. Collaboration with local language experts is expected to develop models that reflect cultural backgrounds.

Privacy and Data Security Issues

Another important issue with AI speech recognition is the protection of personal information and data security . Because speech recognition systems record and analyze what users say, data management and handling require careful attention. In particular, when converting minutes or conversations into text, the data may contain highly confidential materials or information related to databases .

To address this issue, companies must first obtain user consent and then present clear data usage policies . Collected data must be encrypted and access restrictions must be established to minimize the risk of information leaks. Furthermore, when connecting to external services through API integration, it is important to implement thorough security measures and build cooperative relationships with trusted partners .

As the use of AI voice recognition advances, sincerely addressing these issues will be key to gaining user trust.

Addressing the challenge: Onkyo Corporation's voice recognition engine

To address these challenges, such as dialect support and data security , Onkyo Corporation is developing its own AI voice recognition engine based on the concepts of being lightweight, affordable, and easy to use .

Lightweight: Lightweight design that can be used on smartphones

By using a unique algorithm, we have created a lightweight model that can run not only on a server but also on a smartphone, enabling flexible use in real-time processing on-site and in mobile environments.

Cheap: Reduced server load reduces costs

Its lightweight design significantly reduces server load and operational costs . It is easy to implement for businesses of all sizes, and supports the automation and efficiency of speech recognition.

Easy to use: Supports telephone voices and elderly voices

It is optimized for low-bitrate (8bit/8kHz) telephone voice and is available in both batch and streaming versions. Furthermore, dictionary registration and voice learning functions enable it to flexibly handle human-like conversations , including dialects and the speaking styles of elderly people.

High security: private cloud and access restrictions

We also take the utmost care in handling personal information, adopting a private cloud environment and secure communications . By setting access restrictions, we also address the challenges of corporate data management and information protection .

Onkyo's voice recognition technology is expected to be used in a variety of fields , including creating meeting minutes , voice input at logistics sites, and automating business processes through API integration.

For more information, please visit the official website: Onkyo Corporation About Onkyo SPEECH