README.md

---
license: apache-2.0
language:
- ky
- ru
task_categories:
- tabular-classification
- audio-classification
tags:
- keyword-spotting
- wake-word
- kyrgyz
- speech
- mfcc
- spectral-features
- educational
- teaching
size_categories:
- 10K<n<100K
pretty_name: "Akylai KWS Features — Educational Spectral-Feature Dataset"
---

# Akylai KWS Features — An Educational Spectral-Feature Dataset for Keyword Spotting

A ready-to-model, **tabular** dataset for teaching binary classification on a *real* speech
problem: detecting the Kyrgyz wake word **«Акылай»** (*Akylai*) versus everything else.
Each row is one short audio clip already converted into a **fixed-length vector of 250
spectral features**, so students can go straight to `scikit-learn` without touching a single
audio library — yet the problem is a genuine, non-toy **Keyword Spotting (KWS)** task with a
natural class imbalance, several distinct negative sub-populations, and instructive failure
modes.

> **One-line summary.** 40 000 clips → 250 acoustic features → binary label
> (`1` = wake word, `0` = not). Mildly imbalanced (1 : 3). Built for an ML course that has
> just covered linear models (Logistic Regression, SVM) and is about to meet trees.

---

## Table of contents

1. [The task: what is Keyword Spotting?](#1-the-task-what-is-keyword-spotting)
2. [A short history of wake words](#2-a-short-history-of-wake-words)
3. [Where the audio comes from (provenance)](#3-where-the-audio-comes-from-provenance)
4. [From a waveform to a feature vector](#4-from-a-waveform-to-a-feature-vector)
5. [The features, in detail (with formulas)](#5-the-features-in-detail-with-formulas)
6. [Temporal aggregation: variable-length audio → fixed vector](#6-temporal-aggregation-variable-length-audio--fixed-vector)
7. [Dataset schema](#7-dataset-schema)
8. [Modeling challenges (read this before you trust your accuracy)](#8-modeling-challenges)
9. [License, provenance & citation](#9-license-provenance--citation)

---

## 1. The task: what is Keyword Spotting?

**Keyword Spotting (KWS)** is the problem of detecting a small set of predefined words or
short phrases in an audio stream. The most familiar special case is **wake-word detection**
(also *hotword* or *trigger-word* detection): a tiny, always-listening model that waits for a
single phrase — *"Hey Siri"*, *"OK Google"*, *"Alexa"* — and only then wakes up the heavy,
cloud-based speech recogniser.

Formally, given an audio segment $x(t)$ we want a decision function

$$
\hat{y} = \mathbb{1}\!\left[\, p(\text{keyword} \mid x) \ge \tau \,\right],
$$

where $\tau$ is an operating threshold. In this dataset the keyword is the Kyrgyz given name
**«Акылай»** (three syllables, stress on the final *-ай*), and the task is reduced to its
cleanest form: **binary classification of pre-segmented 2-second-scale clips** — keyword vs.
non-keyword.

KWS has several properties that make it a richer teaching example than tabular toy datasets:

- **Strong class imbalance.** In deployment the keyword is vanishingly rare (a wake word may
  fire a handful of times per day against hours of non-keyword audio). Here we use a gentle
  **1 : 3** ratio — enough to make *accuracy misleading* without being degenerate.
- **Asymmetric error costs.** A *false reject* (missing the keyword) annoys the user once; a
  *false accept* (waking up on a TV advert) is far worse. This motivates the whole
  precision/recall/threshold toolkit rather than a single accuracy number.
- **A meaningful feature-engineering step.** Audio is not naturally tabular. Turning a
  waveform into a fixed-length vector is itself a modelling decision — and a great lesson.

---

## 2. A short history of wake words

- **1950s–60s — first isolated-word recognisers.** Bell Labs' *Audrey* (1952) recognised
  spoken digits from a single speaker; IBM's *Shoebox* (1962) handled 16 words. These were
  analog/template machines, but they established the core idea of matching short acoustic
  patterns.
- **1970s–80s — features and dynamic time warping.** The **cepstrum** and then **Mel-Frequency
  Cepstral Coefficients (MFCCs)** (Davis & Mermelstein, 1980) became the standard front-end,
  and **DTW** allowed matching words of different durations.
- **1980s–2000s — statistical models.** **Hidden Markov Models (HMMs)** with Gaussian Mixture
  emissions dominated speech. Classic KWS was often **keyword-filler HMMs**: one model for the
  keyword, a "garbage" model for everything else.
- **2014 — the deep-learning turning point for KWS.** Google's *"Small-footprint keyword
  spotting using deep neural networks"* (Chen, Parada & Heigold, 2014) showed a compact DNN on
  log-mel features beating the HMM pipeline — the recipe behind *"OK Google"* on-device.
- **2014–2017 — the smart-speaker era.** *Amazon Echo / Alexa* (2014), *"Hey Siri"* on a
  dedicated low-power core (2017), and *"OK Google"* turned always-on wake-word detection into
  a mass-market component. Constraints became extreme: a few **tens of kilobytes** of
  parameters, running continuously at milliwatts.
- **2018–present — convolutional & streaming models.** Architectures such as
  **TC-ResNet**, **BC-ResNet**, and depthwise-separable CNNs pushed accuracy up while keeping
  the model tiny enough for an MCU/NPU.

This dataset's parent project trains exactly such a tiny on-device model (a ~30 K-parameter
BC-ResNet) for «Акылай». **The features you have here are the *classical* front-end** —
MFCCs and spectral descriptors — which is both historically faithful and a perfect bridge
from "linear models on tables" to "real speech".

---

## 3. Where the audio comes from (provenance)

The clips come from **four different sources**, recorded in the `source` column. The single
most important thing to understand about this dataset is the split between **synthetic
(text-to-speech, TTS)** audio and **real human** audio.

| `source` | `label` | n | Synthetic / real | What it is |
|---|:---:|---:|---|---|
| `positive` | **1** | 10 000 | **TTS** (in-house Kyrgyz TTS) | The wake word **«Акылай»**, spoken by a Kyrgyz text-to-speech model trained on podcast voices. |
| `base_neg` | 0 | 7 680 | **TTS** (*same* engine as positives) | Other Kyrgyz words/phrases from the **same** TTS voice — *not* the wake word. |
| `confusable` | 0 | 2 949 | **TTS** (KaniTTS, a *different* engine) | Phonetic near-neighbours — words ending in *-ай / -лай / -кай / -бай* (e.g. *Алтынай, калай, чай, лайк*) designed to look "almost" like the keyword. |
| `podcast` | 0 | 19 371 | **Real human speech** | 2-second cuts of continuous Kyrgyz podcast speech — natural, spontaneous, with no wake word. |

**Why this matters (and why we keep `source`).** The negatives are not homogeneous:

- `podcast` is **real, out-of-domain** audio → in practice it is *easy* to separate from the
  synthetic positives, partly for the wrong reason (the model can latch onto "synthetic vs.
  real" timbre rather than the word itself — a classic **shortcut**).
- `base_neg` shares the **exact same TTS voice** as the positives, so the *only* thing
  distinguishing it from a positive is the **word** → this is the **honest, hard** part of the
  problem.
- `confusable` tests robustness to **phonetically similar words**.

The `source` column is **not a feature** — never feed it to the model. It is provided for
**error analysis**: *which kind of negative does your model actually fail on?* (Spoiler from
our baseline experiments: almost all false positives come from `base_neg`.)

> **Wake word.** «Акылай» — a Kyrgyz feminine given name, 3 syllables, stress on `-ай`.
> All audio is 16 kHz mono.

---

## 4. From a waveform to a feature vector

A raw clip is a sequence of $16\,000$ amplitude samples per second — far too high-dimensional
and variable-length to feed to a classifier directly. The standard speech front-end turns it
into a compact, fixed-length descriptor in three stages: **framing**, **time–frequency
transform**, and **feature extraction + aggregation**.

### 4.1 Framing

The signal $x(n)$ is cut into overlapping short frames so that each frame is approximately
**stationary** (the vocal tract changes slowly, ~10 ms scale). We use

$$
N_{\text{fft}} = 512 \;(\approx 32\,\text{ms window}), \qquad H = 160 \;(\approx 10\,\text{ms hop}),
$$

with a **Hann window** $w(n) = 0.5\left(1 - \cos\frac{2\pi n}{N-1}\right)$ to reduce spectral
leakage.

### 4.2 Short-Time Fourier Transform (STFT)

For frame index $m$ and frequency bin $k$,

$$
X(m,k) \;=\; \sum_{n=0}^{N_{\text{fft}}-1} x(n + mH)\, w(n)\, e^{-\,j\,2\pi k n / N_{\text{fft}}}.
$$

From it we form the **magnitude** $|X(m,k)|$ and **power** spectrogram

$$
S(m,k) = |X(m,k)|^2 .
$$

The bin $k$ corresponds to physical frequency $f_k = \dfrac{k}{N_{\text{fft}}}\, f_s$, with
$f_s = 16\,000$ Hz. Everything below is computed **per frame** $m$ and then aggregated over
time (§6).

---

## 5. The features, in detail (with formulas)

We extract **12 groups** of descriptors. They fall into three families:

- **Cepstral** (MFCC + dynamics) — compact model of the spectral *envelope* (the vocal-tract
  shape that defines phonemes).
- **Spectral shape** (centroid, bandwidth, roll-off, flatness, contrast) — interpretable
  scalar summaries of *where* and *how* spectral energy is distributed ("timbre").
- **Energy / harmonicity / time-domain** (RMS, chroma, ZCR) — loudness, pitch-class content,
  and a rough voicing/noisiness cue.

### 5.1 Mel filterbank and log-mel energies

Human pitch perception is roughly logarithmic, captured by the **mel scale**

$$
\text{mel}(f) = 2595 \,\log_{10}\!\left(1 + \frac{f}{700}\right).
$$

We place $M = 40$ overlapping **triangular filters** $H_j(k)$ equally spaced on the mel axis
and integrate the power spectrum through them:

$$
E_{\text{mel}}(m,j) \;=\; \sum_{k} H_j(k)\, S(m,k), \qquad j = 1,\dots,40 .
$$

The dataset stores the **log (dB) mel energies** $\;10\log_{10} E_{\text{mel}}(m,j)\;$
(columns `mel0…mel39`). *Interpretation:* a coarse, perceptually-warped picture of the spectral
envelope — high values in low-mel bands mean energy concentrated at low pitch, etc.

### 5.2 Mel-Frequency Cepstral Coefficients (MFCC)

MFCCs decorrelate the log-mel vector with a **Discrete Cosine Transform (DCT-II)**:

$$
c(m,i) \;=\; \sum_{j=1}^{M} \log E_{\text{mel}}(m,j)\,
\cos\!\left[\frac{\pi i}{M}\left(j - \tfrac{1}{2}\right)\right], \qquad i = 0,\dots,19 .
$$

We keep the first **20** coefficients (`mfcc0…mfcc19`). Low-order coefficients capture the
**smooth spectral envelope** (formant structure → which phoneme is spoken); $c(m,0)$ is
proportional to overall log-energy. MFCCs are the single most important speech feature
historically and are nearly **uncorrelated**, which suits linear models well.

### 5.3 Delta ($\Delta$) and delta-delta ($\Delta\Delta$) coefficients

A single frame says nothing about *motion*. The **delta** features approximate the temporal
derivative of each coefficient with a regression over $\pm\Theta$ frames:

$$
\Delta c(m,i) \;=\; \frac{\displaystyle\sum_{\theta=1}^{\Theta} \theta\,\bigl[c(m+\theta,i) - c(m-\theta,i)\bigr]}
{\displaystyle 2\sum_{\theta=1}^{\Theta} \theta^{2}} .
$$

The **delta-delta** (acceleration) features are the deltas of the deltas. Columns
`d1_0…d1_19` ($\Delta$) and `d2_0…d2_19` ($\Delta\Delta$). *Interpretation:* how fast the
spectrum is changing — crucial for distinguishing a short, dynamic spoken word from
quasi-stationary noise or music.

### 5.4 Spectral centroid

The "centre of mass" of the spectrum — a strong correlate of perceived **brightness**:

$$
\text{centroid}(m) \;=\; \frac{\sum_{k} f_k \,|X(m,k)|}{\sum_{k} |X(m,k)|}.
$$

### 5.5 Spectral bandwidth

The spread of energy around the centroid (here the $p=2$, i.e. standard-deviation, form):

$$
\text{bandwidth}(m) \;=\;
\left( \frac{\sum_{k} |X(m,k)|\,\bigl(f_k - \text{centroid}(m)\bigr)^{2}}
{\sum_{k} |X(m,k)|} \right)^{1/2}.
$$

### 5.6 Spectral roll-off

The frequency $f_R(m)$ below which a fraction $\rho = 0.85$ of the total spectral energy lies:

$$
f_R(m) = \min\Big\{ f_K \;:\; \sum_{k:\,f_k \le f_K} |X(m,k)| \;\ge\; \rho \sum_{k} |X(m,k)| \Big\}.
$$

Separates voiced/low-frequency-dominated frames from broadband/fricative ones.

### 5.7 Spectral flatness

The ratio of the **geometric** to the **arithmetic** mean of the power spectrum — a
*tonality vs. noisiness* measure in $[0,1]$:

$$
\text{flatness}(m) \;=\;
\frac{\exp\!\left(\frac{1}{K}\sum_{k}\ln S(m,k)\right)}{\frac{1}{K}\sum_{k} S(m,k)}.
$$

A value near $1$ ⇒ white-noise-like (flat); near $0$ ⇒ tonal/harmonic (peaky), as in voiced
speech. Very useful for telling speech from noise/music.

### 5.8 Spectral contrast

For each of **7 sub-bands** $b$, the (log) difference between the strongest **peaks** and the
weakest **valleys** in that band:

$$
\text{contrast}(m,b) \;=\; \overline{\text{Peak}}_b(m) \;-\; \overline{\text{Valley}}_b(m),
$$

where the peak/valley terms are the mean log-energies of the top/bottom quantile of bins in
band $b$. High contrast ⇒ clear harmonic structure (formants stand out over the noise floor).
Columns `contrast0…contrast6`. *In our baseline this was the single most informative group.*

### 5.9 Chroma

Projects the spectrum onto the **12 pitch classes** of the equal-tempered scale:

$$
\text{chroma}(m,p) \;=\; \sum_{k\,:\,\text{pitch-class}(f_k)=p} |X(m,k)|, \qquad p = 0,\dots,11 .
$$

Octave-invariant harmonic content. Borrowed from music IR; for speech it is a weak but
non-trivial cue. Columns `chroma0…chroma11`.

### 5.10 Zero-Crossing Rate (ZCR)

A cheap time-domain measure of how often the waveform changes sign within a frame of length $L$:

$$
\text{ZCR}(m) \;=\; \frac{1}{2L}\sum_{n}\bigl|\operatorname{sgn} x(n) - \operatorname{sgn} x(n-1)\bigr|.
$$

High ZCR ⇒ noisy/fricative/unvoiced; low ZCR ⇒ voiced/low-frequency. A classic voicing proxy.

### 5.11 Root-Mean-Square energy (RMS)

Per-frame loudness:

$$
\text{RMS}(m) \;=\; \sqrt{\frac{1}{L}\sum_{n} x(n)^{2}} .
$$

Tracks the speech envelope (syllable onsets/offsets, silences).

---

## 6. Temporal aggregation: variable-length audio → fixed vector

The features above produce **one value per frame**, so a clip is a *matrix* $\phi(m)$ of shape
(features × frames), and clips have **different numbers of frames** (different durations).
Classical models need a **fixed-length** vector. We therefore summarise each per-frame feature
$\phi$ over its $T$ frames by its **mean** and **standard deviation**:

$$
\mu_\phi = \frac{1}{T}\sum_{m=1}^{T} \phi(m), \qquad
\sigma_\phi = \sqrt{\frac{1}{T}\sum_{m=1}^{T}\bigl(\phi(m) - \mu_\phi\bigr)^{2}} .
$$

Hence every group contributes **two** columns per channel (`…_mean`, `…_std`), and *every clip
maps to the same 250-dimensional vector* regardless of length. The `std` summaries turn out to
be very informative — they encode *how much the spectrum moves over time*, which separates a
short spoken word from stationary background. (In our baseline, several `…_std` features rank
at the very top of tree-based feature importances.)

> **Design choice / leakage note.** Clip **duration is deliberately *not* a feature.** In the
> raw corpus all `podcast` negatives are exactly 2.000 s while positives are shorter, so
> duration alone would let a model "cheat". Aggregating to mean/std makes the descriptor
> length-invariant and removes that shortcut. (The deeper *synthetic-vs-real* shortcut remains
> — see §8.)

### Feature budget (250 total)

| Group | Per-frame channels | × {mean, std} | Columns |
|---|---:|---:|---:|
| MFCC | 20 | 2 | 40 |
| $\Delta$ MFCC | 20 | 2 | 40 |
| $\Delta\Delta$ MFCC | 20 | 2 | 40 |
| log-mel energies | 40 | 2 | 80 |
| spectral contrast | 7 | 2 | 14 |
| chroma | 12 | 2 | 24 |
| centroid | 1 | 2 | 2 |
| bandwidth | 1 | 2 | 2 |
| roll-off | 1 | 2 | 2 |
| flatness | 1 | 2 | 2 |
| ZCR | 1 | 2 | 2 |
| RMS | 1 | 2 | 2 |
| **Total** | | | **250** |

---

## 7. Dataset schema

One parquet file, **40 000 rows × 252 columns**, all features `float32`, no missing values.

| Column(s) | Type | Role | Notes |
|---|---|---|---|
| `mfcc{0..19}_{mean,std}` | float32 | feature | cepstral envelope |
| `d1_{0..19}_{mean,std}` | float32 | feature | $\Delta$ MFCC |
| `d2_{0..19}_{mean,std}` | float32 | feature | $\Delta\Delta$ MFCC |
| `mel{0..39}_{mean,std}` | float32 | feature | log-mel energies (dB) |
| `contrast{0..6}_{mean,std}` | float32 | feature | spectral contrast |
| `chroma{0..11}_{mean,std}` | float32 | feature | pitch-class energy |
| `centroid_{mean,std}` | float32 | feature | brightness |
| `bandwidth_{mean,std}` | float32 | feature | spectral spread |
| `rolloff_{mean,std}` | float32 | feature | 85 % roll-off freq. |
| `flatness_{mean,std}` | float32 | feature | tonality |
| `zcr_{mean,std}` | float32 | feature | zero-crossing rate |
| `rms_{mean,std}` | float32 | feature | loudness |
| **`label`** | int | **target** | `1` = «Акылай», `0` = negative |
| **`source`** | string | **metadata** | `positive` / `base_neg` / `confusable` / `podcast` — **do not train on this** |

**Class balance:** 10 000 positive / 30 000 negative (**25 % positive, 1 : 3**).

---

## 8. Modeling challenges

This is where the dataset earns its keep as a teaching tool. Things students *should* run into:

1. **Class imbalance → accuracy lies.** A constant "always negative" classifier already scores
   **75 % accuracy** but is useless (F1 = 0, PR-AUC = 0.25). Insist on **precision, recall,
   F1, ROC-AUC, and especially PR-AUC**, plus the confusion matrix. Tools to discuss:
   `class_weight="balanced"`, threshold tuning, resampling.

2. **The synthetic-vs-real shortcut (the big one).** Positives are TTS; `podcast` negatives are
   real human speech. A model can score deceptively well by learning *"synthetic timbre vs.
   real"* instead of *"the word Akylai vs. other words"*. The antidote is **per-`source` error
   analysis**: evaluate false-positive rate **separately** on `podcast`, `base_neg`, and
   `confusable`. The honest difficulty lives in `base_neg` (same TTS voice, different word).

3. **Operating point & asymmetric costs.** F1 is not the deployment metric; real KWS cares
   about *recall at a fixed false-alarm rate*. Sweep the threshold $\tau$ and read off the
   precision/recall trade-off — a natural lead-in to ROC and PR curves.

4. **Feature scaling matters — but only for some models.** Distance/margin-based learners
   (Logistic Regression, SVM, k-NN) need `StandardScaler`; tree ensembles
   (Random Forest, Gradient Boosting) are scale-invariant. A clean side-by-side lesson.

5. **High dimensionality & redundancy.** 250 features, many correlated (MFCC vs. mel; mean vs.
   std). Good ground for regularisation ($L_1/L_2$), feature importance, and dimensionality
   reduction. Note that in a 2-D **PCA** projection the classes **overlap heavily** — a useful
   reminder that "I can't see a boundary in 2-D" does *not* mean the classes are inseparable in
   250-D.

6. **Speaker / generator leakage.** Positives come from a limited set of synthetic voices; a
   purely random train/test split can leak speaker identity and **inflate** scores. A stricter
   evaluation would split by speaker or by source. Worth at least *discussing*.

7. **It's "easy enough" to be encouraging, hard enough to be real.** Even a linear model
   reaches high PR-AUC on these features, so beginners get a rewarding result quickly — while
   the per-source breakdown leaves a genuine, interpretable hard core to dig into.

---


## 9. License, provenance & citation

- **License:** Apache-2.0.
- **Languages:** Kyrgyz (`ky`), with some Russian (`ru`) confusables.
- **Audio provenance:** positives and `base_neg` are generated by an in-house Kyrgyz
  text-to-speech model (trained on podcast voices); `confusable` clips are generated by
  **KaniTTS**; `podcast` negatives are 2-second cuts of real Kyrgyz-language podcast speech.
  Features were extracted with `librosa` (16 kHz, $N_{\text{fft}}=512$, hop $=160$).
- **Parent project:** the «Акылай» on-device wake-word detector
  (`KaniTTS-research-team/AkylAi_Wake_Word_V4`). This features table is a derived, tabular
  **teaching** snapshot — it does **not** contain audio.

```bibtex
@misc{akylai_kws_features,
  title  = {Akylai KWS Features: An Educational Spectral-Feature Dataset for Keyword Spotting},
  author = {AkylAi Wake Word project},
  year   = {2026},
  note   = {Derived tabular features (MFCC + spectral descriptors) over the Akylai wake-word corpus},
  license = {Apache-2.0}
}
```

> **Educational use.** This dataset is intended for teaching classification and audio feature
> engineering. The synthetic positives and the domain split between TTS and real speech make it
> unsuitable as-is for benchmarking a production wake-word detector.