diff --git a/third_party/__init__.py b/third_party/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/third_party/paddle_audio/__init__.py b/third_party/paddle_audio/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/third_party/paddle_audio/frontend.py b/third_party/paddle_audio/frontend.py
deleted file mode 100644
index 1b337732e..000000000
--- a/third_party/paddle_audio/frontend.py
+++ /dev/null
@@ -1,146 +0,0 @@
-from typing import Tuple
-import numpy as np
-import paddle
-from paddle import Tensor
-from paddle import nn
-from paddle.nn import functional as F
-
-
-def frame(x: Tensor,
-          num_samples: Tensor,
-          win_length: int,
-          hop_length: int,
-          clip: bool = True) -> Tuple[Tensor, Tensor]:
-    """Extract frames from audio.
-
-    Parameters
-    ----------
-    x : Tensor
-        Shape (N, T), batched waveform.
-    num_samples : Tensor
-        Shape (N, ), number of samples of each waveform.
-    win_length : int
-        Window length.
-    hop_length : int
-        Number of samples shifted between ajancent frames.
-    clip : bool, optional
-        Whether to clip audio that does not fit into the last frame, by 
-        default True
-
-    Returns
-    -------
-    frames : Tensor
-        Shape (N, T', win_length).
-    num_frames : Tensor
-        Shape (N, ) number of valid frames
-    """
-    assert hop_length <= win_length
-    num_frames = (num_samples - win_length) // hop_length
-    padding = (0, 0)
-    if not clip:
-        num_frames += 1
-        # NOTE: pad hop_length - 1 to the right to ensure that there is at most
-        # one frame dangling to the righe edge
-        padding = (0, hop_length - 1)
-
-    weight = paddle.eye(win_length).unsqueeze(1)
-
-    frames = F.conv1d(x.unsqueeze(1),
-                      weight,
-                      padding=padding,
-                      stride=(hop_length, ))
-    return frames, num_frames
-
-
-class STFT(nn.Layer):
-    """A module for computing stft transformation in a differentiable way. 
-    
-    Parameters
-    ------------
-    n_fft : int
-        Number of samples in a frame.
-        
-    hop_length : int
-        Number of samples shifted between adjacent frames.
-        
-    win_length : int
-        Length of the window.
-
-    clip: bool
-        Whether to clip audio is necesaary.
-    """
-    def __init__(self,
-                 n_fft: int,
-                 hop_length: int,
-                 win_length: int,
-                 window_type: str = None,
-                 clip: bool = True):
-        super().__init__()
-
-        self.hop_length = hop_length
-        self.n_bin = 1 + n_fft // 2
-        self.n_fft = n_fft
-        self.clip = clip
-
-        # calculate window
-        if window_type is None:
-            window = np.ones(win_length)
-        elif window_type == "hann":
-            window = np.hanning(win_length)
-        elif window_type == "hamming":
-            window = np.hamming(win_length)
-        else:
-            raise ValueError("Not supported yet!")
-
-        if win_length < n_fft:
-            window = F.pad(window, (0, n_fft - win_length))
-        elif win_length > n_fft:
-            window = window[:n_fft]
-
-        # (n_bins, n_fft) complex
-        kernel_size = min(n_fft, win_length)
-        weight = np.fft.fft(np.eye(n_fft))[:self.n_bin, :kernel_size]
-        w_real = weight.real
-        w_imag = weight.imag
-
-        # (2 * n_bins, kernel_size)
-        w = np.concatenate([w_real, w_imag], axis=0)
-        w = w * window
-
-        # (2 * n_bins, 1, kernel_size) # (C_out, C_in, kernel_size)
-        w = np.expand_dims(w, 1)
-        weight = paddle.cast(paddle.to_tensor(w), paddle.get_default_dtype())
-        self.register_buffer("weight", weight)
-
-    def forward(self, x: Tensor, num_samples: Tensor) -> Tuple[Tensor, Tensor]:
-        """Compute the stft transform.
-        Parameters
-        ------------
-        x : Tensor [shape=(B, T)]
-            The input waveform.
-        num_samples : Tensor 
-            Number of samples of each waveform.
-        Returns
-        ------------
-        D : Tensor
-            Shape(N, T', n_bins, 2) Spectrogram.
-
-        num_frames: Tensor
-            Shape (N,) number of samples of each spectrogram
-        """
-        num_frames = (num_samples - self.win_length) // self.hop_length
-        padding = (0, 0)
-        if not self.clip:
-            num_frames += 1
-            padding = (0, self.hop_length - 1)
-
-        batch_size, _, _ = paddle.shape(x)
-        x = x.unsqueeze(-1)
-        D = F.conv1d(self.weight,
-                     x,
-                     stride=(self.hop_length, ),
-                     padding=padding,
-                     data_format="NLC")
-        D = paddle.reshape(D, [batch_size, -1, self.n_bin, 2])
-        return D, num_frames
-
diff --git a/third_party/paddle_audio/frontend/english.wav b/third_party/paddle_audio/frontend/english.wav
new file mode 100644
index 000000000..bb28291f6
Binary files /dev/null and b/third_party/paddle_audio/frontend/english.wav differ
diff --git a/third_party/paddle_audio/frontend/kaldi.py b/third_party/paddle_audio/frontend/kaldi.py
new file mode 100644
index 000000000..9364670fd
--- /dev/null
+++ b/third_party/paddle_audio/frontend/kaldi.py
@@ -0,0 +1,188 @@
+from typing import Tuple
+import numpy as np
+import paddle
+from paddle import Tensor
+from paddle import nn
+from paddle.nn import functional as F
+import soundfile as sf
+
+def read(wavpath:str, sr:int = None, dtype='int16')->Tuple[int, np.ndarray]:
+    wav, r_sr = sf.read(wavpath, dtype=dtype)
+    if sr:
+        assert sr == r_sr
+    return r_sr, wav
+
+ 
+def frames(x: Tensor,
+          num_samples: Tensor,
+          sr: int,
+          win_length: float,
+          stride_length: float,
+          clip: bool = False) -> Tuple[Tensor, Tensor]:
+    """Extract frames from audio.
+
+    Parameters
+    ----------
+    x : Tensor
+        Shape (B, T), batched waveform.
+    num_samples : Tensor
+        Shape (B, ), number of samples of each waveform.
+    sr: int
+        Sampling Rate.
+    win_length : float
+        Window length in ms.
+    stride_length : float
+        Stride length in ms.
+    clip : bool, optional
+        Whether to clip audio that does not fit into the last frame, by 
+        default True
+
+    Returns
+    -------
+    frames : Tensor
+        Shape (B, T', win_length).
+    num_frames : Tensor
+        Shape (B, ) number of valid frames
+    """
+    assert stride_length <= win_length
+    stride_length = int(stride_length * sr)
+    win_length = int(win_length * sr)
+    
+    num_frames = (num_samples - win_length) // stride_length
+    padding = (0, 0)
+    if not clip:
+        num_frames += 1
+        need_samples = num_frames * stride_length + win_length
+        padding = (0, need_samples - num_samples - 1)
+
+    weight = paddle.eye(win_length).unsqueeze(1) #[win_length, 1, win_length]
+
+    frames = F.conv1d(x.unsqueeze(-1),
+                      weight,
+                      padding=padding,
+                      stride=(stride_length, ),
+                      data_format='NLC')
+    return frames, num_frames
+
+
+def _povey_window(frame_len:int) -> np.ndarray:
+    win = np.empty(frame_len)
+    for i in range(frame_len):
+        win[i] = (0.5 - 0.5 * np.cos(2 * np.pi * i / (frame_len - 1)) )**0.85 
+    return win
+
+
+class STFT(nn.Layer):
+    """A module for computing stft transformation in a differentiable way. 
+    
+    http://practicalcryptography.com/miscellaneous/machine-learning/intuitive-guide-discrete-fourier-transform/
+    
+    Parameters
+    ------------ 
+    n_fft : int
+        Number of samples in a frame.
+        
+    sr: int
+        Number of Samplilng rate.
+        
+    stride_length : float
+        Number of samples shifted between adjacent frames.
+        
+    win_length : float
+        Length of the window.
+
+    clip: bool
+        Whether to clip audio is necesaary.
+    """
+    def __init__(self,
+                 n_fft: int,
+                 sr: int,
+                 win_length: float,
+                 stride_length: float,
+                 window_type: str = None,
+                 clip: bool = False):
+        super().__init__()
+        self.sr = sr
+        self.win_length = int(win_length * sr)
+        self.stride_length = int(stride_length * sr)
+        self.clip = clip
+        
+        self.n_fft = n_fft
+        self.n_bin = 1 + n_fft // 2
+
+        # https://github.com/numpy/numpy/blob/v1.20.0/numpy/fft/_pocketfft.py#L49
+        kernel_size = min(self.n_fft, self.win_length)
+        
+        # calculate window
+        if not window_type:
+            window = np.ones(kernel_size)
+        elif window_type == "hann":
+            window = np.hanning(kernel_size)
+        elif window_type == "hamm":
+            window = np.hamming(kernel_size)
+        elif window_type == "povey":
+            window = _povey_window(kernel_size)
+        else:
+            msg = f"{window_type} Not supported yet!"
+            raise ValueError(msg)
+
+        # https://en.wikipedia.org/wiki/Discrete_Fourier_transform
+        # (n_bins, n_fft) complex
+        n = np.arange(0, self.n_fft, 1.)
+        wsin = np.empty((self.n_bin, kernel_size)) #[Cout, kernel_size]
+        wcos = np.empty((self.n_bin, kernel_size)) #[Cout, kernel_size]
+        for k in range(self.n_bin): # Only half of the bins contain useful info
+            wsin[k,:] = np.sin(2*np.pi*k*n/self.n_fft)[:kernel_size]
+            wcos[k,:] = np.cos(2*np.pi*k*n/self.n_fft)[:kernel_size]
+        w_real = wcos
+        w_imag = wsin
+        
+        # https://en.wikipedia.org/wiki/DFT_matrix
+        # https://ccrma.stanford.edu/~jos/st/Matrix_Formulation_DFT.html
+        # weight = np.fft.fft(np.eye(n_fft))[:self.n_bin, :kernel_size]
+        # w_real = weight.real
+        # w_imag = weight.imag
+
+        # (2 * n_bins, kernel_size)
+        #w = np.concatenate([w_real, w_imag], axis=0)
+        w = w_real
+        w = w * window
+
+        # (2 * n_bins, 1, kernel_size) # (C_out, C_in, kernel_size)
+        w = np.expand_dims(w, 1)
+        weight = paddle.cast(paddle.to_tensor(w), paddle.get_default_dtype())
+        self.register_buffer("weight", weight)
+
+    def forward(self, x: Tensor, num_samples: Tensor) -> Tuple[Tensor, Tensor]:
+        """Compute the stft transform.
+        Parameters
+        ------------
+        x : Tensor [shape=(B, T)]
+            The input waveform.
+        num_samples : Tensor [shape=(B,)]
+            Number of samples of each waveform.
+        Returns
+        ------------
+        D : Tensor
+            Shape(B, T', n_bins, 2) Spectrogram.
+
+        num_frames: Tensor
+            Shape (B,) number of samples of each spectrogram
+        """
+        num_frames = (num_samples - self.win_length) // self.stride_length
+        padding = (0, 0)
+        if not self.clip:
+            num_frames += 1
+            need_samples = num_frames * self.stride_length + self.win_length
+            padding = (0, need_samples - num_samples - 1)
+
+        batch_size, _ = paddle.shape(x)
+        x = x.unsqueeze(-1)
+        D = F.conv1d(x, self.weight,
+                     stride=(self.stride_length, ),
+                     padding=padding,
+                     data_format="NLC")
+        #D = paddle.reshape(D, [batch_size, -1, self.n_bin, 2])
+        D = paddle.reshape(D, [batch_size, -1, self.n_bin, 1])
+        return D, num_frames
+
diff --git a/third_party/paddle_audio/frontend/kaldi_test.py b/third_party/paddle_audio/frontend/kaldi_test.py
new file mode 100644
index 000000000..393591ee4
--- /dev/null
+++ b/third_party/paddle_audio/frontend/kaldi_test.py
@@ -0,0 +1,297 @@
+from typing import Tuple
+import numpy as np
+import paddle
+import unittest
+
+import decimal
+import numpy
+import math
+import logging
+from pathlib import Path
+
+from third_party.paddle_audio.frontend import kaldi
+
+def round_half_up(number):
+    return int(decimal.Decimal(number).quantize(decimal.Decimal('1'), rounding=decimal.ROUND_HALF_UP))
+
+def rolling_window(a, window, step=1):
+    # http://ellisvalentiner.com/post/2017-03-21-np-strides-trick
+    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
+    strides = a.strides + (a.strides[-1],)
+    return numpy.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)[::step]
+
+
+def do_dither(signal, dither_value=1.0):
+    signal += numpy.random.normal(size=signal.shape) * dither_value
+    return signal
+    
+def do_remove_dc_offset(signal):
+    signal -= numpy.mean(signal)
+    return signal
+
+def do_preemphasis(signal, coeff=0.97):
+    """perform preemphasis on the input signal.
+
+    :param signal: The signal to filter.
+    :param coeff: The preemphasis coefficient. 0 is no filter, default is 0.95.
+    :returns: the filtered signal.
+    """
+    return numpy.append((1-coeff)*signal[0], signal[1:] - coeff * signal[:-1])
+
+
+def framesig(sig, frame_len, frame_step, dither=1.0, preemph=0.97, remove_dc_offset=True, wintype='hamming', stride_trick=True):
+    """Frame a signal into overlapping frames.
+
+    :param sig: the audio signal to frame.
+    :param frame_len: length of each frame measured in samples.
+    :param frame_step: number of samples after the start of the previous frame that the next frame should begin.
+    :param winfunc: the analysis window to apply to each frame. By default no window is applied.
+    :param stride_trick: use stride trick to compute the rolling window and window multiplication faster
+    :returns: an array of frames. Size is NUMFRAMES by frame_len.
+    """
+    slen = len(sig)
+    frame_len = int(round_half_up(frame_len))
+    frame_step = int(round_half_up(frame_step))
+    if slen <= frame_len:
+        numframes = 1
+    else:
+        numframes = 1 + (( slen - frame_len) // frame_step)
+
+    # check kaldi/src/feat/feature-window.h
+    padsignal = sig[:(numframes-1)*frame_step+frame_len]
+    if wintype is 'povey':
+        win = numpy.empty(frame_len)
+        for i in range(frame_len):
+            win[i] = (0.5-0.5*numpy.cos(2*numpy.pi/(frame_len-1)*i))**0.85     
+    else: # the hamming window
+        win = numpy.hamming(frame_len)
+        
+    if stride_trick:
+        frames = rolling_window(padsignal, window=frame_len, step=frame_step)
+    else:
+        indices = numpy.tile(numpy.arange(0, frame_len), (numframes, 1)) + numpy.tile(
+            numpy.arange(0, numframes * frame_step, frame_step), (frame_len, 1)).T
+        indices = numpy.array(indices, dtype=numpy.int32)
+        frames = padsignal[indices]
+        win = numpy.tile(win, (numframes, 1))
+        
+    frames = frames.astype(numpy.float32)
+    raw_frames = numpy.zeros(frames.shape)
+    for frm in range(frames.shape[0]):
+        frames[frm,:] = do_dither(frames[frm,:], dither)        # dither
+        frames[frm,:] = do_remove_dc_offset(frames[frm,:])      # remove dc offset
+        raw_frames[frm,:] = frames[frm,:]
+        frames[frm,:] = do_preemphasis(frames[frm,:], preemph)    # preemphasize
+
+    return frames * win, raw_frames
+
+
+def magspec(frames, NFFT):
+    """Compute the magnitude spectrum of each frame in frames. If frames is an NxD matrix, output will be Nx(NFFT/2+1).
+
+    :param frames: the array of frames. Each row is a frame.
+    :param NFFT: the FFT length to use. If NFFT > frame_len, the frames are zero-padded.
+    :returns: If frames is an NxD matrix, output will be Nx(NFFT/2+1). Each row will be the magnitude spectrum of the corresponding frame.
+    """
+    if numpy.shape(frames)[1] > NFFT:
+        logging.warn(
+            'frame length (%d) is greater than FFT size (%d), frame will be truncated. Increase NFFT to avoid.',
+            numpy.shape(frames)[1], NFFT)
+    complex_spec = numpy.fft.rfft(frames, NFFT)
+    return numpy.absolute(complex_spec)
+
+
+def powspec(frames, NFFT):
+    """Compute the power spectrum of each frame in frames. If frames is an NxD matrix, output will be Nx(NFFT/2+1).
+
+    :param frames: the array of frames. Each row is a frame.
+    :param NFFT: the FFT length to use. If NFFT > frame_len, the frames are zero-padded.
+    :returns: If frames is an NxD matrix, output will be Nx(NFFT/2+1). Each row will be the power spectrum of the corresponding frame.
+    """
+    return numpy.square(magspec(frames, NFFT))
+
+
+def framesig_without_dither_dc_preemphasize(sig, frame_len, frame_step, wintype='hamming', stride_trick=True):
+    """Frame a signal into overlapping frames.
+
+    :param sig: the audio signal to frame.
+    :param frame_len: length of each frame measured in samples.
+    :param frame_step: number of samples after the start of the previous frame that the next frame should begin.
+    :param winfunc: the analysis window to apply to each frame. By default no window is applied.
+    :param stride_trick: use stride trick to compute the rolling window and window multiplication faster
+    :returns: an array of frames. Size is NUMFRAMES by frame_len.
+    """
+    slen = len(sig)
+    frame_len = int(round_half_up(frame_len))
+    frame_step = int(round_half_up(frame_step))
+    if slen <= frame_len:
+        numframes = 1
+    else:
+        numframes = 1 + (( slen - frame_len) // frame_step)
+
+    # check kaldi/src/feat/feature-window.h
+    padsignal = sig[:(numframes-1)*frame_step+frame_len]
+    
+    if wintype is 'povey':
+        win = numpy.empty(frame_len)
+        for i in range(frame_len):
+            win[i] = (0.5-0.5*numpy.cos(2*numpy.pi/(frame_len-1)*i))**0.85 
+    elif wintype == '':
+        win = numpy.ones(frame_len)
+    elif wintype == 'hann':
+        win = numpy.hanning(frame_len)
+    else: # the hamming window
+        win = numpy.hamming(frame_len)
+        
+    if stride_trick:
+        frames = rolling_window(padsignal, window=frame_len, step=frame_step)
+    else:
+        indices = numpy.tile(numpy.arange(0, frame_len), (numframes, 1)) + numpy.tile(
+            numpy.arange(0, numframes * frame_step, frame_step), (frame_len, 1)).T
+        indices = numpy.array(indices, dtype=numpy.int32)
+        frames = padsignal[indices]
+        win = numpy.tile(win, (numframes, 1))
+        
+    frames = frames.astype(numpy.float32)
+    raw_frames = frames
+    return frames * win, raw_frames
+
+
+def frames(signal,samplerate=16000,winlen=0.025,winstep=0.01,
+          nfilt=40,nfft=512,lowfreq=0,highfreq=None, wintype='hamming'):
+    frames_with_win, raw_frames = framesig_without_dither_dc_preemphasize(signal, winlen*samplerate, winstep*samplerate, wintype)
+    return frames_with_win, raw_frames
+
+
+def complexspec(frames, NFFT):
+    """Compute the magnitude spectrum of each frame in frames. If frames is an NxD matrix, output will be Nx(NFFT/2+1).
+
+    :param frames: the array of frames. Each row is a frame.
+    :param NFFT: the FFT length to use. If NFFT > frame_len, the frames are zero-padded.
+    :returns: If frames is an NxD matrix, output will be Nx(NFFT/2+1). Each row will be the magnitude spectrum of the corresponding frame.
+    """
+    if numpy.shape(frames)[1] > NFFT:
+        logging.warn(
+            'frame length (%d) is greater than FFT size (%d), frame will be truncated. Increase NFFT to avoid.',
+            numpy.shape(frames)[1], NFFT)
+    complex_spec = numpy.fft.rfft(frames, NFFT)
+    return complex_spec
+
+
+def stft_with_window(signal,samplerate=16000,winlen=0.025,winstep=0.01,
+          nfilt=40,nfft=512,lowfreq=0,highfreq=None,dither=1.0,remove_dc_offset=True, preemph=0.97, 
+          wintype='hamming'):
+    frames_with_win, raw_frames = framesig_without_dither_dc_preemphasize(signal, winlen*samplerate, winstep*samplerate, wintype)
+    
+    spec = magspec(frames_with_win, nfft) # nearly the same until this part
+    scomplex = complexspec(frames_with_win, nfft)
+    
+    rspec = magspec(raw_frames, nfft)
+    rcomplex = complexspec(raw_frames, nfft)
+    return spec, scomplex, rspec, rcomplex
+
+
+class TestKaldiFE(unittest.TestCase):
+    def setUp(self):
+        self. this_dir = Path(__file__).parent
+        
+        self.wavpath = str(self.this_dir / 'english.wav')
+        self.winlen=0.025 # ms
+        self.winstep=0.01 # ms
+        self.nfft=512
+        self.lowfreq = 0
+        self.highfreq = None
+        self.wintype='hamm'
+        self.nfilt=40
+        
+        
+    def test_read(self):
+        import scipy.io.wavfile as wav
+        rate, sig = wav.read(self.wavpath)
+        sr, wav = kaldi.read(self.wavpath)
+        self.assertTrue(np.all(sig == wav))
+        self.assertEqual(rate, sr)
+        
+    def test_frames(self):
+        sr, wav = kaldi.read(self.wavpath)
+        _, fs = frames(wav, samplerate=sr, 
+                            winlen=self.winlen, winstep=self.winstep, 
+                            nfilt=self.nfilt, nfft=self.nfft, 
+                            lowfreq=self.lowfreq, highfreq=self.highfreq, 
+                            wintype=self.wintype)
+        
+        t_wav = paddle.to_tensor([wav], dtype='float32')
+        t_wavlen = paddle.to_tensor([len(wav)])
+        t_fs, t_nframe = kaldi.frames(t_wav, t_wavlen, sr, self.winlen, self.winstep, clip=False)
+        t_fs = t_fs.astype(fs.dtype)[0]
+        
+        self.assertEqual(t_nframe.item(), fs.shape[0])
+        self.assertTrue(np.allclose(t_fs.numpy(), fs))
+        
+        
+    def test_stft(self):
+        sr, wav = kaldi.read(self.wavpath)
+        
+        for wintype in ['', 'hamm', 'hann', 'povey']:
+            print(wintype)
+            self.wintype=wintype
+            sftf_win, stft_c_win, _, stft_c = stft_with_window(wav, samplerate=sr, 
+                                winlen=self.winlen, winstep=self.winstep, 
+                                nfilt=self.nfilt, nfft=self.nfft, 
+                                lowfreq=self.lowfreq, highfreq=self.highfreq, 
+                                wintype=self.wintype)
+            print('py', stft_c_win.real)
+            #print(stft_c_win.imag)
+            
+            t_wav = paddle.to_tensor([wav], dtype='float32')
+            t_wavlen = paddle.to_tensor([len(wav)])
+            
+            stft_class = kaldi.STFT(self.nfft, sr, self.winlen, self.winstep, window_type=self.wintype, clip=False)
+            t_stft, t_nframe = stft_class(t_wav, t_wavlen)
+            t_stft = t_stft.astype(sftf_win.dtype)[0]
+            t_real = t_stft[:, :, 0]
+            #t_imag = t_stft[:, :, 1]
+            print('pd', t_real.numpy())
+            #print(t_imag.numpy())
+            
+            self.assertEqual(t_nframe.item(), sftf_win.shape[0])
+
+            self.assertLess(np.sum(t_real.numpy()) - np.sum(stft_c_win.real), 1)
+            print(np.sum(t_real.numpy()))
+            print(np.sum(stft_c_win.real))
+            self.assertTrue(np.allclose(t_real.numpy(), stft_c_win.real, atol=1e-1))
+            #self.assertTrue(np.allclose(t_imag.numpy(), stft_c_win.imag))
+        
+
+
+# from python_speech_features import mfcc
+# from python_speech_features import delta
+# from python_speech_features import logfbank
+# import scipy.io.wavfile as wav
+
+# (rate,sig) = wav.read("english.wav")
+
+# # note that generally nfilt=40 is used for speech recognition
+# fbank_feat = logfbank(sig,nfilt=23,lowfreq=20,dither=0,wintype='povey')
+
+# # the computed fbank coefficents of english.wav with dimension [110,23]
+# # [ 12.2865	12.6906	13.1765	15.714	16.064	15.7553	16.5746	16.9205	16.6472	16.1302	16.4576	16.7326	16.8864	17.7215	18.88	19.1377	19.1495	18.6683	18.3886	20.3506	20.2772	18.8248	18.1899
+# # 11.9198	13.146	14.7215	15.8642	17.4288	16.394	16.8238	16.1095	16.4297	16.6331	16.3163	16.5093	17.4981	18.3429	19.6555	19.6263	19.8435	19.0534	19.001	20.0287	19.7707	19.5852	19.1112
+# # ...
+# # ...
+# # the same with that using kaldi commands: compute-fbank-feats --dither=0.0
+
+
+# mfcc_feat = mfcc(sig,dither=0,useEnergy=True,wintype='povey')
+
+# # the computed mfcc coefficents of english.wav with dimension [110,13]
+# # [ 17.1337	-23.3651	-7.41751	-7.73686	-21.3682	-8.93884	-3.70843	4.68346	-16.0676	12.782	-7.24054	8.25089	10.7292
+# # 17.1692	-23.3028	-5.61872	-4.0075	-23.287	-20.6101	-5.51584	-6.15273	-14.4333	8.13052	-0.0345329	2.06274	-0.564298
+# # ...
+# # ...
+# # the same with that using kaldi commands: compute-mfcc-feats --dither=0.0
+
+
+
+if __name__ == '__main__':
+    unittest.main()
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