Fix docs

README.rst

@@ -21,26 +21,23 @@
 ===========================
 
 This package provides pythonic bindings for Kaldi_ functionality so it can be
-seemlessly integrated with Python-based workflows. It is a part fo the signal-processing and machine learning toolbox
-Bob_.
+seamlessly integrated with Python-based workflows. It is a part fo the signal-
+processing and machine learning toolbox Bob_.
 
 
 Installation
 ------------
 
-To install the package, install firt bob, and then install the bob.kaldi package:
+This package depends on both Bob_ and Kaldi_. To install Bob_ follow our
+installation_ instructions. Kaldi_ is also bundled in our conda channnels which
+means you can install Kaldi_ using conda easily too. After you have installed
+Bob_, please follow these instructions to install Kaldi_ too.
 
-  $ conda install bob kaldi
+  # BOB_ENVIRONMENT is the name of your conda enviroment.
+  $ source activate BOB_ENVIRONMENT
+  $ conda install kaldi
   $ pip install bob.kaldi
 
-To be able to work properly, some dependent packages are required to be installed.
-Please make sure that you have read the `Dependencies
-<https://github.com/idiap/bob/wiki/Dependencies>`_ for your operating system.
-
-This package also requires that Kaldi_ is properly installed alongside the
-Python interpreter you're using, under the directory ``<PREFIX>/lib/kaldi``,
-along with all necessary scripts and compiled binaries.
-
 
 Documentation
 -------------
@@ -48,7 +45,7 @@ Documentation
 For further documentation on this package, please read the `Stable Version
 <http://pythonhosted.org/bob.kaldi/index.html>`_ or the `Latest Version
 <https://www.idiap.ch/software/bob/docs/latest/bioidiap/bob.kaldi/master/index.html>`_
-of the documentation.  For a list of tutorials on this or the other packages ob
+of the documentation.  For a list of tutorials on this or the other packages of
 Bob_, or information on submitting issues, asking questions and starting
 discussions, please visit its website.
 

doc/index.rst

@@ -48,9 +48,9 @@ MFCC Extraction
 ---------------
 
 Two functions are implemented to extract MFCC features
-`bob.kaldi.mfcc` and `bob.kaldi.mfcc_from_path`. The former function
-accepts the speech samples as `numpy.ndarray`, whereas the latter the
-filename as `str`, returning the features as `numpy.ndarray`:
+:py:any:`bob.kaldi.mfcc` and :py:any:`bob.kaldi.mfcc_from_path`. The former function
+accepts the speech samples as :py:any:`numpy.ndarray`, whereas the latter the
+filename as :py:any:`str`, returning the features as :py:any:`numpy.ndarray`:
 
 1. `bob.kaldi.mfcc`