What is automatic speech recog­ni­tion (ASR)?

Automatic speech recog­ni­tion is a process for au­to­mat­i­cal­ly con­vert­ing speech into text. ASR tech­nolo­gies use machine learning methods to analyze, process and output speech patterns as text. From gen­er­at­ing meeting tran­scrip­tions and subtitles to virtual voice as­sis­tants, automatic speech recog­ni­tion is suitable for a wide range of use cases.

What does automatic speech recog­ni­tion mean?

Automatic speech recog­ni­tion (ASR) is a subfield of computer science and com­pu­ta­tion­al lin­guis­tics focused on de­vel­op­ing methods that au­to­mat­i­cal­ly translate spoken language into a machine-readable form. When the output is in text form, it’s also referred to as Speech-to-Text (STT). ASR methods are based on sta­tis­ti­cal models and complex al­go­rithms.

How does automatic speech recog­ni­tion work?

Automatic speech recog­ni­tion consists of multiple con­sec­u­tive steps that seam­less­ly integrate. Below we outline each phase:

1. Capturing speech (automatic speech recog­ni­tion): The system captures spoken language through a mi­cro­phone or other audio source.
2. Pro­cess­ing speech (natural language pro­cess­ing): First, the audio recording is cleaned of back­ground noise. Then, an algorithm analyzes the phonetic and phonemic char­ac­ter­is­tics of the language. Next, the captured features are compared to pre-trained models to identify in­di­vid­ual words.
3. Gen­er­at­ing text (speech to text): In the final step, the system converts the rec­og­nized sounds into text.

Close

Comparing ASR al­go­rithms — Hybrid approach vs. deep learning

There are generally two main ap­proach­es to automatic speech recog­ni­tion. In the past, tra­di­tion­al hybrid ap­proach­es like sto­chas­tic Hidden Markov models were primarily used. Recently, however, deep learning tech­nolo­gies have been in­creas­ing­ly employed, as the precision of tra­di­tion­al models has plateaued.

Tra­di­tion­al hybrid approach

Tra­di­tion­al models require force-aligned data, meaning they use the text tran­scrip­tion of an audio speech segment to determine where specific words occur. The tra­di­tion­al hybrid approach combines a lexicon model, an acoustic model and a language model to tran­scribe speech:

• The lexicon model defines the phonetic pro­nun­ci­a­tion of words. A separate data or phoneme set must be created for each language.
• The acoustic model focuses on modeling the acoustic patterns of the language. Using force-aligned data, it predicts which sound or phoneme cor­re­sponds to different segments of speech.
• The language model learns which word sequences are most common in a language, aiming to predict the words most likely to come next in a given sequence.

The main drawback of the hybrid approach is the dif­fi­cul­ty in in­creas­ing the accuracy of speech recog­ni­tion using this method. Ad­di­tion­al­ly, training three separate models is very time- and cost-intensive. However, due to the extensive knowledge available on how to create a robust model using this approach, many companies still go for this option.

Deep learning with end-to-end processes

End-to-end systems can directly tran­scribe a sequence of acoustic input features. The algorithm learns how to convert spoken words using a large amount of paired data. The data pairs are comprised of an audio file con­tain­ing a spoken sentence and the cor­re­spond­ing tran­scrip­tion of the sentence.

Deep learning ar­chi­tec­tures such as CTC, LAS and RNNT can be trained to deliver precise results even without using force-aligned data, lexicon models or language models. Many deep learning systems are still paired with a language model though, as it can further enhance tran­scrip­tion accuracy.

The end-to-end approach for automatic speech recog­ni­tion offers greater accuracy than tra­di­tion­al models. These ASR systems are also easier to train and require less human labor.

What are the main ap­pli­ca­tions for automatic speech recog­ni­tion?

Thanks to advances in machine learning, ASR tech­nolo­gies are becoming in­creas­ing­ly accurate and more powerful. Automatic speech recog­ni­tion can be used across various in­dus­tries to increase ef­fi­cien­cy, improve customer sat­is­fac­tion and/or boost ROI. The most important areas of ap­pli­ca­tion include:

• Telecom­mu­ni­ca­tions: Contact centers use ASR tech­nolo­gies to tran­scribe and analyze customer con­ver­sa­tions. Accurate tran­scrip­tions are also needed for call tracking and for phone solutions im­ple­ment­ed via cloud servers.
• Video platforms: The creation of real-time subtitles on video platforms has now become an industry standard. Automatic speech recog­ni­tion is also helpful for content cat­e­go­riza­tion.
• Media mon­i­tor­ing: ASR APIs make it possible to analyze TV shows, podcasts, radio broad­casts and other types of media for brand or topic mentions.
• Video con­fer­enc­ing: Meeting solutions like Zoom, Microsoft Teams and Google Meet rely on accurate tran­scrip­tions and content analysis to generate key insights and guide relevant actions. Automatic speech recog­ni­tion can also provide live subtitles for video con­fer­ences.
• Voice as­sis­tants: Virtual as­sis­tants like Amazon Alexa, Google Assistant and Apple’s Siri rely on automatic speech recog­ni­tion. This tech­nol­o­gy allows the as­sis­tants to answer questions, perform tasks and interact with other devices.

What role does ar­ti­fi­cial in­tel­li­gence play in ASR tech­nolo­gies?

Ar­ti­fi­cial In­tel­li­gence helps improve the accuracy and overall func­tion­al­i­ty of ASR systems. In par­tic­u­lar, the de­vel­op­ment of large language models has led to a sig­nif­i­cant im­prove­ment in pro­cess­ing natural language. A large language model can not only perform trans­la­tions and create complex texts that are highly relevant, it can also recognize spoken language. ASR systems benefit greatly from ad­vance­ments in this area. AI is also ben­e­fi­cial for the de­vel­op­ment of accent-specific language models.

What are the strengths and weak­ness­es of automatic speech recog­ni­tion?

Compared to tra­di­tion­al tran­scrip­tion, automatic speech recog­ni­tion offers several ad­van­tages. A key strength of modern ASR processes is their high accuracy, stemming from the ability to train these systems with large datasets. This enables improved quality in subtitles or tran­scrip­tions, which can also be provided in real time.

Another major benefit is increased ef­fi­cien­cy. Automatic speech recog­ni­tion allows companies to scale, expand their service offerings faster and reach a larger customer base. ASR tools also make it easier for students and pro­fes­sion­als to document audio content, for example, during a business meeting or uni­ver­si­ty lecture.

While more accurate than ever before, ASR systems still cannot match human accuracy though. This is largely due to the many nuances in spoken language. Accents, dialects, tone vari­a­tions and back­ground noise remain chal­leng­ing for these systems, with even the most powerful deep learning models unable to handle input that doesn’t match expected or typical patterns. Another concern is that ASR tech­nolo­gies often process personal data, raising issues regarding privacy and data security.