Speech Recognition Research Papers per Year

Research on speech recognition is large and still growing. OpenAlex lists 28,220 works published in 2025 that contain the phrase "speech recognition" ¹. The table below gives the 2020 figure for each phrase. The field is not only bigger: the vocabulary is shifting. "Speech-to-text" and "voice assistant" grew much faster than "dictation", which is the older word for writing by voice.

The figures are phrase searches, not a reading list. Nothing was screened by hand, and a work counts if it uses the words anywhere OpenAlex indexes them. Treat the numbers as a way to compare topics and years, not as exact totals.

Speech recognition is the largest field, and automatic speech recognition is its engineering core

The broad phrase "speech recognition" dominates every other row. The narrower engineering term "automatic speech recognition" has 10,003 works in 2025 ². That is the vocabulary of researchers who build and benchmark recognizers, so its growth tracks the machine learning work behind modern dictation.

Overlap matters here. A paper that says "automatic speech recognition" almost always also says "speech recognition", so these two rows are not separate piles of work and must not be added together. The same applies to every row: one paper can appear under several phrases.

Speech-to-text and voice assistants grew fastest

Two phrases stand out for how far they moved in five years. "Speech-to-text" reached 9,278 works in 2025 ³. "Voice assistant" reached 6,728 works ⁴.

Both rows describe applications rather than algorithms. That fits what happened in the field: recognition accuracy became good enough that researchers began studying what people do with it, in assistants, in transcription tools and in apps. "Speech-to-text" is also the plain-language name a product team or a non-specialist uses, which widens the pool of authors who write it.

The ranking by growth is a reading of the rows above, not a calculated statistic. Every row below has both its 2025 figure and its 2020 figure, so you can compare them directly.

Dictation grew slowly and has stopped climbing

"Dictation" is a large, older literature: 3,139 works in 2025 ⁵. It grew, but far more slowly than speech-to-text. Its peak year in the collector's history is 2024, not 2025, so the count has flattened.

Two reasons are plausible, and the data cannot separate them. First, the word "dictation" is used outside speech technology: in medicine, in language teaching (dictation as a classroom exercise) and in law. A steady stream of such works would dilute any technology-driven growth. Second, newer work may simply prefer other words, such as "speech-to-text", "voice input" or "transcription". The rows show that the vocabulary moved. They do not show that people write less by voice.

Text entry and voice typing are small but rising

"Text entry" is the human-computer interaction term for how people get words into a device, and it covers keyboards, swipe, handwriting and speech. It has 1,508 works in 2025 ⁶. "Voice typing" is smaller still, at 475 works in 2025 ⁷.

"Voice typing" is the phrase closest to what an everyday writer does, and it is rare in scholarship. That is a gap worth knowing about. Most studies of speech input compare it with typing under other names, so a search on this one phrase understates the real volume of such work. The text entry literature is the better place to find controlled comparisons of speed and errors between speech and keyboards.

Whisper shows how one model can move a field

"Whisper speech" returned 293 works in 2025 ⁸. Read this row with care. The query is two ordinary words, and "whisper" is also what people do with their voices, so some of these works are about whispered speech and not about the Whisper recognition model. The row is a rough signal of the model's influence, not a count of papers that use it.

Even with that caution, the 2025 figure is much higher than the 2020 one, which is consistent with a widely used open recognition model giving many researchers a common tool.

Accessibility and medicine are tiny niches in this measure

Two specialist rows are small. "Speech recognition accessibility" has only 4 works in 2025 ¹⁰. "Medical speech recognition" has 20 works ⁹, and its peak was 2023.

These counts are small for a reason that has nothing to do with importance. A quoted phrase search needs the exact words to appear. Studies of clinical documentation or of voice tools for disabled users often use other wording ("clinical dictation", "voice input", "assistive technology"), so the exact phrase misses them. Do not conclude that medicine or accessibility are neglected. Conclude only that these two phrases are rarely used.

Where the rows disagree, and what they cannot tell you

The rows do not disagree about direction: nearly everything rose. They disagree about pace, and about whether the word "dictation" still leads. The honest reading is that attention moved toward speech-to-text, assistants and large recognition models.

Limits to keep in mind:

• Recent years are incomplete. OpenAlex keeps adding works after a year ends, so 2025 will keep growing, and 2024 and 2025 are not yet comparable to 2020 on equal footing.
• A phrase counts any use of the words. A paper that mentions "speech recognition" once in passing counts the same as a paper devoted to it.
• Rows overlap. One paper can sit in several rows, so totals across rows would double count.
• Volume is not quality. The data says nothing about how accurate recognition is, how fast people write with it, or whether the studies are good.
• The index changes. OpenAlex coverage of preprints, theses and books varies by year, which can create growth that is partly an artifact of indexing.

What this means if you are choosing how to write

The research supports a practical conclusion: speech input is mature enough that a large and growing body of science studies it, and the tools behind it are the same ones the research describes. For a writer, the volume of research is a reason to expect better recognition over time, not a measure of how good any one tool is today.

To judge a method for yourself, do not rely on how much has been published. Read the controlled comparisons on speed and errors, which live in the text entry literature, and try a tool on your own voice, vocabulary and room. For background on how modern recognizers work and where they fail, see our overview of speech recognition.