Cách HolosAccent Chuyển Giọng Nói Của Bạn Thành Điểm Phát Âm

Preparing your recording

First, the audio is decoded into a standard format: mono sound sampled at 16 kHz.

The system estimates the recording’s signal-to-noise ratio. If that estimate falls below 25 dB and noise filtering is available, it applies a noise-reduction filter before analysis. If filtering fails, it continues with the original recording.

The waveform is then normalized to match the speech analysis model’s input requirements. Silence and recordings shorter than half a second receive a zero score.

Cho phép các cách phát âm khác nhau

Next, the reference text is converted into phonetic pronunciations using a pronunciation engine. A word can have multiple accepted forms.

The pronunciation set includes:

• Các biến thể tiếng Anh Anh và tiếng Anh Mỹ.
• Các dạng rút gọn phổ biến, chẳng hạn như nguyên âm không nhấn mạnh trong to.
• Các lựa chọn ngữ cảnh được chọn lọc.

For example, the final consonant in close the door differs from the one in a close friend. The system can choose among the supported alternatives as it processes the sentence.

Lắng nghe âm thanh, không tái tạo từ

The recording passes through an acoustic model. It produces evidence for different speech sounds across time, at roughly 20-millisecond intervals.

This is a phonetic representation of the recording. The scorer does not use a word-level language model to reconstruct the sentence.

The system then examines those acoustic outputs in two ways:

Phương pháp	Mô tả
Expected-sound alignment	Tìm thời gian tốt nhất cho các âm thanh mong đợi, chọn một dạng phát âm đầy đủ cho mỗi từ.
Independent sound recognition	Theo dõi các âm thanh mà mô hình ưa thích mà không cần câu tham chiếu.

Both views are necessary because alignment can place an expected sound somewhere in a recording even when the speaker omitted it. Independent sound recognition supplies additional evidence about what was actually spoken.

Matching also takes timing into account, making it harder for a sound in one word to count as evidence for a distant word.

Đánh giá từng âm thanh

For each aligned sound, the scorer calculates a pronunciation accuracy measure: how strongly the model supports the expected sound relative to competing sounds during that interval.

It also considers:

• The expected sound’s peak probability.
• Whether independent sound recognition found a matching sound nearby.

A fitted mathematical mapping turns this evidence into a sound accuracy value between 0 and 100.

A sound missing from the independent recognition result does not automatically receive zero. The acoustic evidence still matters.

Quyết định từ nào cần chú ý

Word feedback combines these sound values with explicit error checks. A word must:

• Have enough evidence that it was spoken.
• Meet the configured average accuracy threshold.
• Avoid detected substitutions, insertions, or severe missing-sound errors.

Clear substitutions require weak support for the expected sound and strong support for a competing sound.

Bằng chứng	Ngưỡng hiện tại
Support for the expected sound	Below 0.05
Support for its replacement	At least 0.5

These are model evidence thresholds, not probabilities that a human listener would judge the word incorrect.

For flagged words, the system returns the expected pronunciation and its estimate of the sounds heard, written in the International Phonetic Alphabet (IPA). That estimate can be imperfect, especially around word boundaries.

Xây dựng điểm câu

The sentence score starts with a fitted combination of:

• Average sound accuracy.
• The share of expected sounds matched by independent sound recognition.
• The number of speech sounds pronounced per second.
• Long pauses between words.

The timing calculation excludes silence before the first aligned sound and after the last.

These features contribute to one overall score. The current implementation does not provide separate user-facing fluency, completeness, or intonation scores.

Cách trọng số từ ảnh hưởng đến kết quả

The scorer then applies an explicit word-credit multiplier. Each word is weighted by the number of sounds in its selected pronunciation.

Kết quả từ	Trọng số nhận được
Correct	Full weight
Spoken but flagged	One quarter of its weight
Omitted	No credit

word credit =
    (correct sound weight + 0.25 × flagged spoken sound weight)
    / total expected sound weight

raw score =
    sentence estimate, clipped to 0–100, × word credit

Consequently, a long incorrect word usually costs more than a short one. Reading only a small part of the sentence also limits the score, even if that fragment sounds clear.

So sánh điểm của bạn với bản tham chiếu

Finally, the system can normalize the raw score using a reference recording of the same phrase.

It scores the synthesized reference with the same scorer version and, when a usable matching baseline is already available, applies:

displayed score = min(100, raw score / reference score × 100)

For example, a raw score of 80 against a reference score of 88 becomes approximately 90.9.

This adjustment accounts for the fact that the model may give even the reference recording less than 100.

Baselines are tied to:

• The reference voice and speech-generation model.
• The scoring model.
• The vocabulary.
• The calibration and rule version.

If a matching baseline is unavailable or unusable, the result keeps its raw score. A first attempt can therefore remain unadjusted while a baseline is prepared for later attempts.

Ý nghĩa của điểm số của bạn

The resulting number is a practice score on HolosAccent’s scale.

A score of 90 does not mean that 90 percent of your words were correct, and it does not establish a CEFR level or native-speaker equivalence.

The mapping has been fitted using expert-rated recordings, but the new scoring system remains experimental and its full acceptance criteria have not yet been verified. It can miss errors or flag acceptable speech.

Individual word feedback and patterns across repeated attempts provide context for interpreting the number.